Programming and scripting languages: general Books

Book SynopsisDeploy, manage, and secure containers and containerized applications on Google Cloud Platform (GCP). This book covers each container service in GCP from the ground up and teaches you how to deploy and manage your containers on each service. You will start by setting up and configuring GCP tools and the tenant environment. You then will store and manage Docker container images with GCP Container Registry (ACR). Next, you will deploy containerized applications with GCP Cloud Run and create an automated CI/CD deployment pipeline using Cloud Build. The book covers GCP''s flagship service, Google Kubernetes Service (GKE), and deployment of a Kubernetes cluster using clear steps and considering GCP best practices using the GCP management console and gcloud command-line tool. Also covered is monitoring containers and containerized applications on GCP with Cloud Monitoring, and backup and restore containers and containerized applications on GCP. By the endTable of ContentsChapter 1: Get Started with Google Cloud Platform (GCP)Chapter Goal: Setup and configure GCP tools and tenant environment No of pages: 40 Sub -Topics1. Set up your Google Cloud Platform (GCP) tenant2. Understanding GCP projects3. Understanding cloud shell4. Secure and manage your GCP account (projects and more)5. GCP Services overview Chapter 2: Store and Manage Docker Container Images with GCP Container Registry (ACR)Chapter Goal: Here we learn how to Store Docker Container images on GCP Container registry No of pages: 40Sub - Topics 1. Setup GCP Container Registry2. Push Docker images to Container Registry3. Pull images from GCP Container Registry4. Manage and secure GCP Container RegistryChapter 3: Deploy Containerized Applications with GCP Cloud RunChapter Goal: This chapter explains how to deploy containers and containerized applications on GCP cloud run No of pages: 40Sub - Topics: 1. Set up GCP cloud run 2. Deploy containers with cloud run3. Use cloud build and git to deploy containers 4. Scale containerized applications on cloud run5. Monitor and manage containerized applications on cloud runChapter 4: Deploy Containerized Applications with Google Kubernetes Engine (GKE)Chapter Goal: This chapters explains how to deploy containers and containerized applications with GKENo of pages:Sub - Topics: 1. Getting started with GKE2. Setup and configure GKE networking and storage3. Deploy Kubernetes dashboard (Web UI) on GKE4. Manage and secure GKE5. Run Batch jobs on Kubernetes with batch (beta)Chapter 5: Deploy Docker Containers on GCP Compute Engine Chapter Goal: This chapter explains how to deploy containers and containerized applications on GCP compute engineNo of pages: 40Sub - Topics: 1. Install Docker container host on Ubuntu Linux VM 2. Install Docker container host on Windows server 2019 VM3. Deploy containers on GCP compute engine using GCP container-optimized OSChapter 6: Secure your GCP Environment and Containers Chapter Goal: This chanpters explains how to secure and protect containers and containerized applications on GCPNo of pages: 40Sub - Topics: 1. Introduction to GCP identify infrastructure 2. Setup organization policies 3. Roles, service accounts and auditing capabilities4. GCP networking and firewalls configuration Chapter 7: Scale Containers and Containerized Applications on GCPChapter Goal: This chapter explains how to scale containers and containerized applications on GCPNo of pages: 40Sub - Topics: 1. Scale Google Kubernetes Service (GKE)2. Scale cloud run and cloud build containers 3. Scale GCP Container Registry4. Scale compute engine hosts and containers Chapter 8: Monitor Containers and Containerized Applications on GCP with Stackdriver MonitoringChapter Goal: Learn how to Monitor Containers and Containerized Applications on GCPNo of pages: 40Sub - Topics: 1. Monitor Google Kubernetes Service (GKE) 2. Monitor cloud run containers 3. Monitor compute engine resources 4. GCP cost management and tools Chapter 9: Backup and Restore Containers and Containerized Applications on GCPChapter Goal: This chapter explains how to backup and restore containers and containerized applications on GCPNo of pages: 40Sub - Topics: 1. Backup persistent storage disks 2. Backup compute engine resources 3. Manage cloud storage and file store Chapter 10: Troubleshooting Containers and Containerized Applications on GCP Chapter Goal: This chapters explains how to troubleshoot containers and containerized applications issues on GCPNo of pages: 40Sub - Topics: 1. Troubleshoot Google Kubernetes Service (GKE)2. Troubleshoot cloud run and cloud build deployments 3. Troubleshoot GCP Container Registry5. Troubleshoot compute engine resource

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Book SynopsisImplement numerical algorithms in Java using NM Dev, an object-oriented and high-performance programming library for mathematics.You'll see how it can help you easily create a solution for your complex engineering problem by quickly putting together classes.Numerical Methods Using Java covers a wide range of topics, including chapters on linear algebra, root finding, curve fitting, differentiation and integration, solving differential equations, random numbers and simulation, a whole suite of unconstrained and constrained optimization algorithms, statistics, regression and time series analysis. The mathematical concepts behind the algorithms are clearly explained, with plenty of code examples and illustrations to help even beginners get started. What You Will Learn Program in Java using a high-performance numerical library Learn the mathematics for a wide range of numerical computing algorithms Trade Review“The book is primarily a user’s guide to the NM DEV commercial software library … .” (Anthony J. Duben, Computing Reviews, December 6, 2022)Table of ContentsTable of ContentsAbout the Authors...........................................................................................................iPreface............................................................................................................................ii1. Why Java?..............................................................................................................61.1. Java in 2020.....................................................................................................61.2. Java vs. C++....................................................................................................61.3. Java vs. Python................................................................................................61.4. Java in the future .............................................................................................62. Data Structures.......................................................................................................72.1. Function...........................................................................................................72.2. Polynomial ......................................................................................................73. Linear Algebra .......................................................................................................83.1. Vector and Matrix ...........................................................................................83.1.1. Vector Properties .....................................................................................83.1.2. Element-wise Operations.........................................................................83.1.3. Norm ........................................................................................................93.1.4. Inner product and angle ...........................................................................93.2. Matrix............................................................................................................103.3. Determinant, Transpose and Inverse.............................................................103.4. Diagonal Matrices and Diagonal of a Matrix................................................103.5. Eigenvalues and Eigenvectors.......................................................................103.5.1. Householder Tridiagonalization and QR Factorization Methods..........103.5.2. Transformation to Hessenberg Form (Nonsymmetric Matrices)...........104. Finding Roots of Single Variable Equations .......................................................114.1. Bracketing Methods ......................................................................................114.1.1. Bisection Method ...................................................................................114.2. Open Methods...............................................................................................114.2.1. Fixed-Point Method ...............................................................................114.2.2. Newton’s Method (Newton-Raphson Method) .....................................114.2.3. Secant Method .......................................................................................114.2.4. Brent’s Method ......................................................................................115. Finding Roots of Systems of Equations...............................................................125.1. Linear Systems of Equations.........................................................................125.2. Gauss Elimination Method............................................................................125.3. LU Factorization Methods ............................................................................125.3.1. Cholesky Factorization ..........................................................................125.4. Iterative Solution of Linear Systems.............................................................125.5. System of Nonlinear Equations.....................................................................126. Curve Fitting and Interpolation............................................................................146.1. Least-Squares Regression .............................................................................146.2. Linear Regression..........................................................................................146.3. Polynomial Regression..................................................................................146.4. Polynomial Interpolation...............................................................................146.5. Spline Interpolation .......................................................................................147. Numerical Differentiation and Integration...........................................................157.1. Numerical Differentiation .............................................................................157.2. Finite-Difference Formulas...........................................................................157.3. Newton-Cotes Formulas................................................................................157.3.1. Rectangular Rule....................................................................................157.3.2. Trapezoidal Rule....................................................................................157.3.3. Simpson’s Rules.....................................................................................157.3.4. Higher-Order Newton-Coles Formulas..................................................157.4. Romberg Integration .....................................................................................157.4.1. Gaussian Quadrature..............................................................................157.4.2. Improper Integrals..................................................................................158. Numerical Solution of Initial-Value Problems....................................................168.1. One-Step Methods.........................................................................................168.2. Euler’s Method..............................................................................................168.3. Runge-Kutta Methods...................................................................................168.4. Systems of Ordinary Differential Equations.................................................169. Numerical Solution of Partial Differential Equations..........................................179.1. Elliptic Partial Differential Equations...........................................................179.1.1. Dirichlet Problem...................................................................................179.2. Parabolic Partial Differential Equations........................................................179.2.1. Finite-Difference Method ......................................................................179.2.2. Crank-Nicolson Method.........................................................................179.3. Hyperbolic Partial Differential Equations.....................................................1710..................................................................................................................................1811..................................................................................................................................1912. Random Numbers and Simulation ....................................................................2012.1. Uniform Distribution .................................................................................2012.2. Normal Distribution...................................................................................2012.3. Exponential Distribution............................................................................2012.4. Poisson Distribution ..................................................................................2012.5. Beta Distribution........................................................................................2012.6. Gamma Distribution ..................................................................................2012.7. Multi-dimension Distribution ....................................................................2013. Unconstrainted Optimization ............................................................................2113.1. Single Variable Optimization ....................................................................2113.2. Multi Variable Optimization .....................................................................2114. Constrained Optimization .................................................................................2214.1. Linear Programming..................................................................................2214.2. Quadratic Programming ............................................................................2214.3. Second Order Conic Programming............................................................2214.4. Sequential Quadratic Programming...........................................................2214.5. Integer Programming.................................................................................2215. Heuristic Optimization......................................................................................2315.1. Genetic Algorithm .....................................................................................2315.2. Simulated Annealing .................................................................................2316. Basic Statistics..................................................................................................2416.1. Mean, Variance and Covariance................................................................2416.2. Moment......................................................................................................2416.3. Rank...........................................................................................................2417. Linear Regression .............................................................................................2517.1. Least-Squares Regression..........................................................................2517.2. General Linear Least Squares....................................................................2518. Time Series Analysis ........................................................................................2618.1. Univariate Time Series..............................................................................2618.2. Multivariate Time Series ...........................................................................2618.3. ARMA .......................................................................................................2618.4. GARCH .....................................................................................................2618.5. Cointegration .............................................................................................2619. Bibliography .....................................................................................................2720. Index .....................................................................................................

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Book SynopsisTable of ContentsChapter 1: An Introduction to Streaming DataChapter Goal: Introduce the readers to the concept of streaming data, the various challenges associated with it, some of its real-world business applications, various windowing techniques along with the concepts of incremental and online learning algorithms. This chapter will also help in understanding the concept of model evaluation in case of streaming data and provide and introduction to the Scikit-Multiflow framework in Python.No of pages- 35Sub -Topics1. Streaming data2. Challenges of streaming data3. Concept drift4. Applications of streaming data5. Windowing techniques6. Incremental learning and online learning7. Illustration : Adopting batch learners into incremental learners8. Introduction to Scikit-Multiflow framework9. Evaluation of streaming algorithmsChapter 2: Change DetectionChapter Goal: Help the readers to understand the various change detection/concept drift detection algorithms and its implementation on various datasets using Scikit-Multiflow.No of pages : 35Sub - Topics: 1. Change detection problem2. Concept drift detection algorithms3. ADWIN4. DDM5. EDDM6. Page HinkleyChapter 3: Supervised and Unsupervised Learning for Streaming DataChapter Goal: Help the readers to understand the various regression and classification (including Ensemble Learning) algorithms for streaming data and its implementation on various datasets using Scikit-Multiflow. Also, discuss some approaches for clustering with streaming data and its implementation using Python.No of pages: 35Sub - Topics: 1. Regression with streaming data2. Classification with streaming data3. Ensemble Learning with streaming data4. Clustering with streaming dataChapter 4: Other Tools and the Path ForwardChapter Goal: Introduce the readers to the other open source tools for handling streaming data such as Spark streaming, MOA and more. Also, educate the reader about additional reading for advanced topics within streaming data analysis.No of pages: 35Sub - Topics: 1. Other tools for handling streaming data1.1.1. Apache Spark1.1.2. Massive Online Analysis (MOA)1.1.3. Apache Kafka2. Active research areas and breakthroughs in streaming data analysis3. Conclusion

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Book SynopsisGet a head start on making your games efficiently by avoiding common design and development pitfalls. Video games combine art and programming; this unique position has opened up opportunities for many pitfalls. This book takes you through the fundamentals of game making and the usual mistakes and bad practices that can harm your games. We start with the common difficulties and challenges, ways to find the gaps, and game design. Next, we discuss game engines and other tools you need to choose while making a game, how you should choose them, and the design documents you need to make. We also cover simple but important tweaks in game mechanics as well as the look and feel of your game. We will also discuss conventions for naming, code structuring, project structuring, and coding. Your thought process will be guided in a way that you can look for the proper approach to make a successful game. The book sheds light upon how to improve the overall game experience and finTable of ContentsChapter 1: Don't Chapter Goal: In this chapter, you will get introduced to the core difficulties and challenges of the game making journey. Chapter 2: The Fault in Our StarsChapter Goal: This chapter will discuss the first encounters with games as well as the process of finding the gaps and lacking. We will also discuss game analysis in this chapter. Chapter 3: Don't Reinvent the Wheel Chapter Goal: We will discuss the game engine and the purpose they serve as powerful and necessary tools in game development.Chapter 4: Choose Your ArsenalChapter Goal: We will learn about game engines in greater detail including their pros n cons by building a RPG.Chapter 5: It's All in My Head/ Don't Keep It All in Your Head Chapter Goal: Learn to make Game Design Document, writing practices, and hurdles that may be stopping you from writing it. Chapter 6: A Stitch in Time Saves Nine Chapter Goal: This chapter teaches conventions of file naming, structuring, project structuring, coding (with some examples)Chapter 7: Git GoodChapter Goal: We will learn about version controlChapter 8: Get Smart!Chapter Goal: This chapter shows the ill effects of hard code. This would enable you to differentiate between good and bad approaches towards code and learn how to eliminate them.Chapter 9: Game Design - The Three musketeers! Chapter Goal: You will learn the overall journey of game design, understanding all the components, their role, and how to roll them properly. Chapter 10: Game Feel and Effects Chapter Goal: We will learn about camera effect, audio effect, particle effect in this chapter.Chapter 11: Input MattersChapter Goal: Learn about choosing input styles properly.Chapter 12: Help!Chapter Goal: This chapter will helping you transcend through reality (pseudo code), dynamic difficulty.Chapter 13: Platform Choosing (Pros n Cons)Chapter Goal: This chapter will help you choose the right platform to publish your game.Chapter 14: The Great Filter/Fermi Paradox Chapter Goal: In this chapter, you will learn how to test your game and how to approach publishing and assessing the success.Chapter 15: Gameover - The Myth of Sisyphus/Insanity Loop/Core LoopChapter Goal: In the final chapter, we will evaluate all we have learnt by studying this game, its success, and the future

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Book SynopsisThis module is not sponsored by IBM and must be installed separately from the Db2 database.After reading Foundation Db2 and Python you'll be able to install Db2 on Windows or Linux, and perform backups and restore data.Table of ContentsChapter 1: Introduction Chapter Goal: An introduction to Db2 for Linux and Windows.No. of Pages: 10Sub-Topics:Glossary of termsIntroduction to the Db2Obtaining the install files for Db2What you need as far as your hardware and OS for your Db2 machineHow to organize your file system to support Db2 data basesChapter 2: Installing Db2Chapter Goal: Describes how to install Db2 on Linux and WindowsNo. of Pages: 50Sub-Topics:Preparing your server for Db2Installing Db2 on LinuxInstalling Db2 on WindowsInstalling the ibm_db moduleTest the connection between Python/ibm_db and Db2Chapter 3: Db2 ManagementChapter Goal: Create Python scripts to access Db2No. of Pages: 40Sub-Topics:Layout of the typical Python script to access Db2The ibm_db moduleThe order of processing in almost all Python scripts accessing Db2Using exceptions in your Python scriptsChapter 4: Installing the Db2 sample database and a custom databaseChapter Goal: Learn how to design and create your own databasesNo. of Pages: 35Sub-Topics:Install the Db2 sample databaseWrite some simple Python scripts to access the sample data baseDesign and install the Orbital Launch data baseWrite some simple Python scripts to assess the Orbital Launch data baseChapter 5: Creating Utility Modules for Accessing Db2Chapter Goal: Creating modules that access ibm_dbNo. of Pages: 40Sub-Topics:Create you first utility moduleUse functions or classes in your module, or both?Organizing your module(s)Accessing your moduleChapter 6: Documenting the ibm_db ModuleChapter Goal: Documenting the ibm_db Module APINo. of Pages: 40Sub-Topics:Document each API in the ibm_db moduleDocument all input and outputs to each APIProvide multiple examples for each APIChapter 7: Writing Good SQL for Db2 (this might cover multiple chapters)Chapter Goal: Creating optimized SQL for Db2No. of Pages: 60Sub-Topics:Minimize the passes through the dataCode for concurrencyLocking and isolation levelsHow to avoid writing codeThe importance of indexesOptimizationSequential vs. random data accessTypes of joinsChapter 8: Where is the ibm_db Module GoingChapter Goal: Explain why IBM does not directly support the ibm_db module No. of Pages: 25Sub-Topics:Why ibm_db is open source?How does ibm_db use underlying systems?Why is this module not included with Db2?Some future items to be added to ibm_db moduleChapter 9: Db2 provided utilitiesChapter Goal: Describe some of the utilities that come with Db2 and possibly some extra cost utilitiesNo. of Pages: 50Subtopics:What utilities are covered has yet to be identifiedChapter 10: BLOB data, what is it and how do you use itChapter Goals: Describe what a blob is and how to use oneNo. of Pages: 50Subtopics:Describe the different kinds of BLOBsAccessing BLOB data in PythonWhat kinds of data can be stored in a BLOBUtilizing BLOBs to store Python data, how to keep data and metadata together in Db2

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Book Synopsis1. An Introduction to Java and its History.- 2. Preparing your Development Environment.- 3. Getting Your Feet Wet.- 4. Java Syntax.- 5. Data Types.- 6. Operators.- 7. Controlling the Flow.- 8. The Stream API.- 9. Debugging, Testing, and Documenting.- 10. Making Your Application Interactive.- 11. Working With Files.- 12. The Publish-Subscribe Framework.- 13. Garbage Collection.- Appendix A.Table of ContentsChapter 1: An Introduction to Java- When every version was released, how were they called and what were the particularities- What is Java, how it is executed, what type of language it is and what is it good for- Chapter 2: Preparing your development environment- Installing Java, choosing an editor, choosing a build tool- Chapter 3: Getting your feet wet- Writing a simple program, compile and execute- Adding a dependency of somebody else’s code through dependencies of existing libraries- Mention best tools for java and most used frameworks like SpringChapter 4: Java syntax- what is a package, module- class- enums- interface ( private methods & default methods)- class, constructor, methods… etc- removal of _ Chapter 5: Data Types- primitive, object types (emphasis on String, Collections, Calendar API)- String – compact Strings- Collections: Immutable collections, factory methods for Collections(JEP 269)- mention Generics- optional – enhancements - threads, futures – CompletableFuture (JEP 266)Chapter 6: Operators- unary, binary, ternary, logic, and the diamond operator (used in conjunction with anonymous inner classes)Chapter 7: Controlling the flow - if, loops- try catch (try with resources with managed variables)- recursionChapter 8: The Stream API - streams , optional to Stream, enhancementsChapter 9: Debugging , testing and documenting- what is a break point- loggers : unified JVM logging (JEP 264)- mocks and stubs- jmc, jps, jcmd – JDK utilities- The new Doclet API- the JShell Command Line Tool- accessing the process API- @Deprecated enhancements (JEP 277)Chapter 10: Making your application interactive- request data with System.in- Swing- Web applications (use the new HTTP client)- JavaFX UI (JEP 253)- Internationalization (JEP 267)Chapter 11: Writing files- storing data to files, reading it from them- serialization to Binary, XML, JSON, YML (JEP290)- playing with images – multi-resolution APIChapter 12: Publish-Subscribe Framework- reactive streamsChapter 13: Garbage Collection- JEP 214,248,271,291

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Book SynopsisCover all the machine learning techniques relevant for forecasting problems, ranging from univariate and multivariate time series to supervised learning, to state-of-the-art deep forecasting models such as LSTMs, recurrent neural networks, Facebook''s open-source Prophet model, and Amazon''s DeepAR model.Rather than focus on a specific set of models, this book presents an exhaustive overview of all the techniques relevant to practitioners of forecasting. It begins by explaining the different categories of models that are relevant for forecasting in a high-level language. Next, it covers univariate and multivariate time series models followed by advanced machine learning and deep learning models. It concludes with reflections on model selection such as benchmark scores vs. understandability of models vs. compute time, and automated retraining and updating of models. Each of the models presented in this book is covered in depth, with an intuitive simple explanation of thTable of ContentsPART I: Machine Learning for ForecastingChapter 1: Models for ForecastingChapter Goal: Explains the different categories of models that are relevant for forecasting in high level languageNo pages: 10Sub -Topics1. Time series models2. Supervised vs unsupervised models3. Classification vs regression models4. Univariate vs multivariate modelsChapter 2: Model Evaluation for ForecastingChapter Goal: Explains model evaluation with specific adaptations to keep in mind for forecastingNo pages: 15Sub -Topics1. Train test split2. Cross validation for forecasting3. BacktestingPART II: Univariate Time Series ModelsChapter 3: The AR ModelChapter Goal: explain the AR model (intuitively, mathematically and give python application with code and data set)No pages: 8Sub -Topics1. Understanding AR model2. Mathematical explanation of the AR model3. Worked out Python forecasting example with the AR modelChapter 4: The MA modelChapter Goal: explain the MA model (intuitively, mathematically and give python application with code and data set)No pages: 8Sub -Topics1. Understanding MA model2. Mathematical explanation of the MA model3. Worked out Python forecasting example with the MA modelChapter 5: The ARMA modelChapter Goal: explain the ARMA model (intuitively, mathematically and give python application with code and data set)No pages: 8Sub -Topics1. Understanding ARMA model2. Mathematical explanation of the ARMA model3. Worked out Python forecasting example with the ARMA modelChapter 6: The ARIMA modelChapter Goal: Explains the ARIMA model (intuitively, mathematically and give python application with code and data set)No pages: 8Sub -Topics1. Understanding ARIMA model2. Mathematical explanation of the ARIMA model3. Worked out Python forecasting example with the ARIMA modelChapter 7: The SARIMA ModelChapter Goal: Explains the SARIMA model (intuitively, mathematically and give python application with code and data set)No pages: 8Sub -Topics1. Understanding SARIMA model2. Mathematical explanation of the SARIMA model3. Worked out Python forecasting example with the SARIMA modelPART III: Multivariate Time Series ModelsChapter 8: The VAR modelChapter Goal: Explains the VAR model (intuitively, mathematically and give python application with code and data set)No pages: 8Sub -Topics1. Understanding VAR model2. Mathematical explanation of the VAR model3. Worked out Python forecasting example with the VAR modelChapter 9: The Bayesian VAR modelChapter Goal: Explains the Bayesian VAR model (intuitively, mathematically and give python application with code and data set)No pages: 8Sub -Topics1. Understanding Bayesian VAR model2. Mathematical explanation of the Bayesian VAR model3. Worked out Python forecasting example with the Bayesian VAR modelPART IV: Supervised Machine Learning ModelsChapter 10: The Linear Regression modelChapter Goal: Explains the Linear Regression model (intuitively, mathematically and give python application with code and data set)No pages: 8Sub -Topics1. Understanding Linear Regression model2. Mathematical explanation of the Linear Regression model3. Worked out Python forecasting example with the Linear Regression modelChapter 11: The Decision Tree modelChapter Goal: Explains the Decision Tree model (intuitively, mathematically and give Python application with code and data set)No pages: 8Sub -Topics1. Understanding Decision Tree model2. Mathematical explanation of the Decision Tree model3. Worked out Python forecasting example with the Decision Tree modelChapter 12: The k-Nearest Neighbors VAR modelChapter Goal: explain the k-Nearest Neighbors (intuitively, mathematically and give python application with code and data set)No pages: 8Sub -Topics1. Understanding k-Nearest neighbors model2. Mathematical explanation of the k-Nearest neighbors model3. Worked out Python forecasting example with the k-Nearest neighbors modelChapter 13: The Random Forest ModelChapter Goal: explain the Random Forest (intuitively, mathematically and give python application with code and data set)No pages: 8Sub -Topics1. Understanding Random Forest model2. Mathematical explanation of the Random Forest model3. Worked out Python forecasting example with the Random Forest modelChapter 14: The XGBoost modelChapter Goal: Explains the XGBoost model (intuitively, mathematically and give python application with code and data set)No pages: 10Sub -Topics1. Understanding XGBoost model2. Mathematical explanation of the XGBoost model3. Worked out Python forecasting example with the XGBoost modelChapter 15: The Neural Network modelChapter Goal: Explains the Neural Network model (intuitively, mathematically and give python application with code and data set)No pages: 10Sub -Topics1. Understanding Neural Network model2. Mathematical explanation of the Neural Network model3. Worked out Python forecasting example with the Neural Network modelPart V: Advanced Machine and Deep Learning ModelsChapter 16: Recurrent Neural NetworksChapter Goal: Explains Recurrent Neural Networks (intuitively, mathematically and give python application with code and data set)No pages: 10Sub -Topics1. Understanding Recurrent Neural Networks2. Mathematical explanation of Recurrent Neural Networks 3. Worked out Python forecasting example with Recurrent Neural Networks Chapter 17: LSTMsChapter Goal: Explains LSTMs (intuitively, mathematically and give python application with code and data set)No pages: 10Sub -Topics1. Understanding LSTMs2. Mathematical explanation of LSTMs 3. Worked out Python forecasting example with LSTMs Chapter 18: Facebook’s Prophet modelChapter Goal: Explains Facebook’s Prophet model (intuitively, mathematically and give Python application with code and data set)No pages: 10Sub -Topics1. Understanding Facebook’s Prophet model2. Mathematical explanation of Facebook’s Prophet model3. Worked out Python forecasting example with Facebook’s Prophet modelChapter 19: Amazon’s DeepAR ModelChapter Goal: Explains Amazon’s DeepAR model (intuitively, mathematically and give python application with code and data set)No pages: 10Sub -Topics1. Understanding Amazon’s DeepAR model2. Mathematical explanation of Amazon’s DeepAR model3. Worked out Python forecasting example with Amazon’s DeepAR modelChapter 20: Deep State Space ModelsChapter Goal: Explains Deep State Space models (intuitively, mathematically and give Python application with code and data set)No pages: 10Sub -Topics1. Understanding Deep State Space models2. Mathematical explanation of Deep State Space models3. Worked out Python forecasting example with Deep State Space modelsChapter 21: Model selectionChapter Goal: Give elements to select the best model for a specific situationNo pages: 16Sub -Topics1. Benchmark scores vs understandability of models vs compute time 2. Black swan outlier problems3. Automated retraining and updating of models4. Conclusion

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Book SynopsisLearn the ins and outs of decisions, biases, and reliability of AI algorithms and how to make sense of these predictions. This book explores the so-called black-box models to boost the adaptability, interpretability, and explainability of the decisions made by AI algorithms using frameworks such as Python XAI libraries, TensorFlow 2.0+, Keras, and custom frameworks using Python wrappers. You'll begin with an introduction to model explainability and interpretability basics, ethical consideration, and biases in predictions generated by AI models. Next, you'll look at methods and systems to interpret linear, non-linear, and time-series models used in AI. The book will also cover topics ranging from interpreting to understanding how an AI algorithm makes a decisionFurther, you will learn the most complex ensemble models, explainability, and interpretability using frameworks such as Lime, SHAP, Skater, ELI5, etc. Moving forward, youwill be introduced to model explainability for unstructurTrade Review“Practical explainable AI using Python combines textbook and cookbook elements. It provides explanations of concepts along with practical examples and exercises. … this book offers a comprehensive foundation that will remain relevant for some time. However, readers should supplement their knowledge with the latest research in order to stay up to date in this dynamic field.” (Gulustan Dogan, Computing Reviews, August 21, 2023)“While the book presents just fundamental aspects, I find this to be a great advantage. Indeed, even the layperson to AI/ML can use this work: the author starts with the most basic definitions and models, and then provides software examples … . This way a very broad readership is possible, since more advanced parts of the chapters will be interesting even for specialists in AI/ML who would like to increase their expertise in the title topic.” (Piotr Cholda, Computing Reviews, April 17, 2023)Table of ContentsChapter 1: Introduction to Model Explainability and InterpretabilityChapter Goal: This chapter is to understand what is model explainability and interpretability using Python. No of pages: 30-40 pages Chapter 2: AI Ethics, Biasness and Reliability Chapter Goal: This chapter aims at covering different frameworks using XAI Python libraries to control biasness, execute the principles of reliability and maintain ethics while generating predictions.No of pages: 30-40Chapter 3: Model Explainability for Linear Models Using XAI ComponentsChapter Goal: This chapter explains use of LIME, SKATER, SHAP and other libraries to explain the decisions made by linear models for supervised learning task, for structured dataNo of pages : 30-40Chapter 4: Model Explainability for Non-Linear Models using XAI ComponentsChapter Goal: This chapter explains use of LIME, SKATER, SHAP and other libraries to explain the decisions made by non-linear models, such as tree based models for supervised learning task, for structured dataNo of pages: 30-40Chapter 5: Model Explainability for Ensemble Models Using XAI ComponentsChapter Goal: This chapter explains use of LIME, SKATER, SHAP and other libraries to explain the decisions made by ensemble models, such as tree based ensemble models for supervised learning task, for structured data No of pages: 30-40Chapter 6: Model Explainability for Time Series Models using XAI ComponentsChapter Goal: This chapter explains use of LIME, SKATER, SHAP and other libraries to explain the decisions made by time series models for structured data, both univariate time series model and multivariate time series modelNo of pages: 30-40Chapter 7: Model Explainability for Natural Language Processing using XAI ComponentsChapter Goal: This chapter explains use of LIME, SKATER, SHAP and other libraries to explain the decisions made by models from text classification, summarization, sentiment classification No of pages: 30-40Chapter 8: AI Model Fairness Using What-If ScenarioChapter Goal: This chapter explains use of Google’s WIT Tool and custom libraries to explain the fairness of an AI modelNo of pages: 30-40Chapter 9: Model Explainability for Deep Neural Network ModelsChapter Goal: This chapter explains use of Python libraries to interpret the neural network models and deep learning models such as LSTM models, CNN models etc. using smooth grad and deep shiftNo of pages: 30-40Chapter 10: Counterfactual Explanations for XAI modelsChapter Goal: This chapter aims at providing counterfactual explanations to explain predictions of individual instances. The "event" is the predicted outcome of an instance, the "cause" are the particular feature values of this instance that were the input to the model that "caused" a certain prediction.No of pages: 30-40Chapter 11: Contrastive Explanation for Machine LearningChapter Goal: In this chapter we will use foil trees: a model-agnostic approach to extracting explanations for finding the set of rules that causes the explanation to be predicted the actual outcome (fact) instead of the other (foil)No of pages: 20-30Chapter 12: Model-Agnostic Explanations By Identifying Prediction InvarianceChapter Goal: In this chapter we will use anchor-LIME (a-LIME), a model-agnostic technique that produces high-precision rule-based explanations for which the coverage boundaries are very clear.No of pages: 20-30Chapter 13: Model Explainability for Rule based Expert System Chapter Goal: In this chapter we will use anchor-LIME (a-LIME), a model-agnostic technique that produces high-precision rule-based explanations for which the coverage boundaries are very clear.No of pages: 20-30Chapter 14: Model Explainability for Computer Vision.Chapter Goal: In this chapter we will use Python libraries to explain computer vision tasks such as object detection, image classification models.No of pages: 20-30

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Book SynopsisTable of ContentsBasic Python for Data Management, Finance, and Marketing1. Getting started with Python- Variables and numeric data types - Python containers to hold our data- Definite loops- Building a mortgage calculator2. Writing your own Python scripts- Custom functions- Indefinite loops- Immutable containers - tuples- Pseudo code to problem solving3. Extending Excel with Python- Dictionaries- Reading and writing csv files with Python- CSV and Urllib modules 4. Data Analysis with NumPy and Pandas- NumPy as an extension of Python - Series and DataFrame- Manipulate and plot data with Pandas5. Solving common problems with Pandas - Concatinate and merge datasets - Group by keys- How to use logic in Pandas 6. Gathering data with Python- Web Scraping with Python - Reading PDF files with Python- Cleaning data- Storing data7. Building predictive models - User authentication- Linear reression- K-nearest- CART – decision tree- Matplotlib to plot data8. Automating tasks with Python- Sending emails- Deploying your script to a server- Running tasks at specific time

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Book SynopsisLearn to program with Rust 2021 Edition, in an easy, step-by-step manner on Unix, the Linux shell, macOS, and the Windows command line.  As you read this book, you''ll build on the knowledge you gained in previous chapters and see what Rust has to offer.   Beginning Rust starts with the basics of Rust, including how to name objects, control execution flow, and handle primitive types. You''ll see how to do arithmetic, allocate memory, use iterators, and handle input/output. Once you have mastered these core skills, you''ll work on handling errors and using the object-oriented features of Rust to build robust Rust applications in no time.Only a basic knowledge of programming in C or C++ and familiarity with a command console are required. After reading this book, you''ll be ready to build simple Rust applications.What You Will Learn Get started programming with Rust Understand heterogeneous data structures aTable of Contents1. Introduction2. Printing on Terminal3. Doing Arithmatic4. Naming Objects5. Controlling Execution Flow6. Using Data Sequences7. Using Primitive Types8. Enumerating Cases9. Using Heterogeneous Data Structures10. Defining Functions11. Defining Generic Functions and Structs12. Allocating Memory13. Data Implementation14. Defining Closures15. Using Changeable Strings16. Ranges and Slices17. Using Iterators18. Input/Output and Error Handling19. Using Traits20. Object-Oriented Programming21. Standard Library Collections22. Drops, Moves, and Copies23. Borrowing and Lifetimes24. More about Lifetimes

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Book SynopsisLearn the fundamentals of the Java 17 LTS or Java Standard Edition version 17 Long Term Support release, including basic programming concepts and the object-oriented fundamentals necessary at all levels of Java development.Table of Contents1. Programming Concepts 2. Setting Up the Environment 3. Writing Java Programs 4. Data Types 5. Operators 6. Statements 7. Classes and Objects 8. Methods 9. Constructors 10. Modules11. Object and Objects Classes 12. Wrapper Classes 13. Execution Handling 14. Assertions 15. Strings 16. Dates and Times 17. Formatting Data 18. Regular Expressions 19. Arrays 20. Inheritance 21. Interfaces 22. Enum Types 23. Java Shell Appendix A: Character Encodings Appendix B: Documentation Comments

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Book SynopsisIntermediate user levelTable of ContentsChapter 1: Extracting the DataChapter Goal: Understanding the potential data sources to build NLP applications for business benefits and ways to extract the text data with examplesNo of pages: 23Sub - Topics: 1. Data extraction through API2. Reading HTML page, HTML parsing3. Reading pdf file in python4. Reading word document5. Regular expressions using python6. Handling strings using python7. Web scrapingChapter 2: Exploring and Processing the Text DataChapter Goal: Data is never clean. This chapter will give in depth knowledge about how to clean and process the text data. It covers topics like cleaning, tokenizing and normalizing text data.No of pages: 22Sub - Topics 1 Text preprocessing methods 2 Data cleaning – punctuation removal, stopwords removal, spelling correction3 Lexicon normalization – stemming and lemmatization4 Tokenization 5 Dealing with emoticons and emojis6 Exploratory data analysis7 End to end text processing pipeline implementationChapter 3: Text to FeaturesChapter Goal: One of the important task with text data is to transform text data into machines or algorithms understandable form, by using different feature engineering methods (basic to advanced).No of pages: 40Sub - Topics 1 One hot encoding2 Count vectorizer3 N grams4 Co-occurrence matrix5 Hashing vectorizer6 TF-IDF7 Word Embedding - Word2vec, fasttext8 Glove embeddings 9 ELMo10 Universal Sentence Encoder11 Understanding Transformers like BERT, GPT12 Open AIsChapter 4: Implementing Advanced NLPChapter Goal: Understanding and building advanced NLP techniques to solve the business problems starting from text similarity to speech recognition and language translation.No of pages: 25Sub - Topics: 1. Noun phrase extraction2. Text similarity3. Parts of speech tagging4. Information extraction – NER – entity recognition 5. Topic modeling6. Machine learning for NLP – a. Text classification7. Sentiment analysis8. Word sense disambiguation9. Speech recognition and speech to text10. Text to speech11. Language detection and translationChapter 5: Deep Learning for NLPChapter Goal: Unlocking the power of deep learning on text data. Solving few real-time applications of deep learning in NLP.No of pages: 55Sub - Topics: 1. Fundamentals of deep learning2. Information retrieval using word embedding’s 3. Text classification using deep learning approaches (CNN, RNN, LSTM, Bi-directional LSTM) 4. Natural language generation – prediction next word/ sequence of words using LSTM. 5. Text summarization using LSTM encoder and decoder. 6. Sentence comparison using SentenceBERT 7. Understanding GPT 8. Comparison between BERT, RoBERTa, DistilBERT, XLNetChapter 6: Industrial Application with End to End Implementation Chapter Goal: Solving real time NLP applications with end to end implementation using python. Right from framing and understanding the business problem to deploying the model.No of pages: 90Sub - Topics: 1. Consumer complaint classification 2. Customer reviews sentiment prediction 3. Data stitching using text similarity and record linkage 4. Text summarization for subject notes 5. Document clustering 6. Product360 - Sentiment, emotion & trend capturing system 7. TED Talks segmentation & topics extraction using machine learning 8. Fake news detection system using deep neural networks 9. E-commerce search engine & recommendation systems using deep learning10. Movie genre tagging using multi-label classification 11. E-commerce product categorization using deep learning12. Sarcasm detection model using CNN13. Building chatbot using transfer learning14. Summarization system using RNN and reinforcement learningChapter 7: Conclusion - Next Gen NLP & AIChapter Goal: So far, we learnt how NLP when coupled with machine learning and deep learning helps us solve some of the complex business problems across industries and domains. In this chapter let us uncover how some of the next generation algorithms that would potentially play important roles in the future NLP era.

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Book SynopsisGo from competent C++ developer to skilled designer or architect using this book as your personal C++ design master class. Updated for the C++20 standard, this title will guide you through the design and implementation of an engaging case study that forms the backdrop for learning the art of applying design patterns and modern C++ techniques to create a high quality, robust application.  Starting with a quick exploration of the requirements for building the application, you''ll delve into selecting an appropriate architecture, eventually designing and implementing all of the necessary modules to meet the project''s requirements. By the conclusion of Practical C++ Design, you''ll have constructed a fully functioning calculator capable of building and executing on any platform that supports both Qt and C++20. Access to the complete source code will help speed your learning. Utilize the Model-View-Controller pattern as the basis for the architecTable of ContentsPreface (5 pages)The preface details my motivation for writing the book, the target audience for the book, thegeneral structure of the book, and how to contact the author. Of particular importance is therationale behind choosing the case study, the target language (C++), and the GUI toolkit (Qt).Chapter 1: Defining the Case Study (6 pages)The first chapter describes, in detail, the case study to be examine in the book. The chapterdiscusses requirements in the abstract and then transitions to the calculator’s specific requirements.This sets the stage for the remainder of the book, which describes, in detail, the design andimplementation of the calculator, pdCalc, proposed in Chapter 1.1. A Brief Introduction2. A Few Words About Requirements3. Reverse Polish Notation (RPN)4. The Calculator’s Requirements5. The Source Code1The advice, information, and conclusions discussed in this book are those of the author and have not beenendorsed by, or reflect the opinions or practices of, ExxonMobil Corporation or its affiliates.5Chapter 2: Decomposition (18 pages)In this chapter, I explain the elements of a good decomposition and strategies for decomposing aproblem into manageable pieces. Subsequently, an architecture for pdCalc is selected, the calculatoris modularized, and use cases are used to develop interfaces for the high level calculator modules.The four high level modules are the stack, the command dispatcher, the user interface (subdividedinto a command line interface and a graphic user interface), and a plugin manager.1. The Elements of a Good Decomposition2. Selecting An Architecture3. Interfaces4. Assessment of Our Current Design5. Next StepsChapter 3: The Stack (20 pages)The stack is the first module discussed in detail. The stack is the fundamental data repositoryof the calculator. As part of the calculator’s design and implementation, the singleton pattern isexplored. The stack also affords the first opportunity to discuss an event system for the calculator,which provides a backdrop for exploration of the observer pattern, including the design andimplementation of reusable publisher and observer abstract classes.1. Decomposition of the Stack Module2. The Stack Class3. Adding Events4. A Quick Note on TestingChapter 4: The Command Dispatcher (32 pages)This chapter describes the design and implementation of the command dispatcher, the module ofthe calculator responsible for the creation, storage, and execution of commands. Of particular notein this chapter is the exposition on the command pattern and how it can be used to implement apractical undo/redo framework. In addition to exploring a traditional deep hierarchy method forimplementing commands, a C++11 alternative using lambda expressions and the standard functiontemplate are presented as a modern alternative design.1. The Decomposition of the Command Dispatcher2. The Command Class3. The Command Repository4. The Command Manager5. The Command Dispatcher6. Revisiting Earlier Decisions6Chapter 5: The Command Line Interface (14 pages)This chapter marks an important milestone, the creation of the first user executable program.In addition to building a simple command line interface, we’ll explore how to create an abstractsoftware interface suitable for both a command line interface and a graphical user interface. Withinthe context of the command line interface, we’ll learn techniques for simple parsing and tokenizingof input text streams.1. The User Interface Abstraction2. The Concrete CLI Class3. Tying It Together: A Working ProgramChapter 6: The Graphical User Interface (24 pages)In this chapter, we build the Qt-based graphical user interface for the calculator. Here, we’llexamine different strategies for building GUIs, abstraction of GUI elements, and modularization ofthe overall GUI design. Included in the discussion is design for the separation of on-screen widgetsfrom look-and-feel.1. Requirements2. Building GUIs3. Modularization4. A Working Program5. A Microsoft Windows Build NoteChapter 7: Plugins (38 pages)In this chapter, I describe how to build a cross-platform plugin system. This system includes theabstract interface for C++ plugins as well as the operating system specific mechanics involved withloading plugins and executing plugin functions. In the concrete case of the plugin loader, I explainthe many build tricks that can be used to handle cross-platform code and demonstrate how theabstract factory pattern provides an elegant design solution to this problem.1. What Is a Plugin?2. Problem 1: The Plugin Interface3. Problem 2: Loading Plugins4. Problem 3: Retrofitting pdCalc5. Incorporating Plugins6. A Concrete Plugin7. Next Steps7Chapter 8: New Requirements (24 pages)Any developer who has ever worked on a production software project quickly learns that newrequirements are always added late in the development cycle. In this chapter, we explore theaddition of new user requests after the original requirements have already been satisfied. Thediscussion progresses from fully implemented solutions to design only solutions to vague ideas forthe reader to explore on her own.1. Fully Designed New Features2. Designs Toward a More Useful Calculator3. Some Interesting Extensions for Self-ExplorationAppendix A: Acquiring, Building, and Executing pdCalc (4 pages)This appendix explains how to download the source code from GitHub and how to build the casestudy on Linux and Windows. Once the program is built, readers will want to execute the codeand its included test suite; execution instructions are therefore provided.1. Getting The Source Code2. Dependencies3. Building pdCalc4. Executing pdCalcAppendix B: Organization of the Source Code (6 pages)This appendix simply explains the organization of the source tree for pdCalc. This appendix isuseful for finding the locations for the source files referenced in the text.1. The src Directory2. The test DirectoryReferences (2 pages)This section lists twenty-nine references cited in the book.Index (3 pages)This section is a complete index for the book.

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Book SynopsisLearn the core aspects of NumPy, Matplotlib, and Pandas, and use them to write programs with Python 3. This book focuses heavily on various data visualization techniques and will help you acquire expert-level knowledge of working with Matplotlib, a MATLAB-style plotting library for Python programming language that provides an object-oriented API for embedding plots into applications.You'll begin with an introduction to Python 3 and the scientific Python ecosystem. Next, you'll explore NumPy and ndarray data structures, creation routines, and data visualization. You'll examine useful concepts related to style sheets, legends, and layouts, followed by line, bar, and scatter plots. Chapters then cover recipes of histograms, contours, streamplots, and heatmaps, and how to visualize images and audio with pie and polar charts.Moving forward, you'll learn how to visualize with pcolor, pcolormesh, and colorbar, and how to visualize in 3D in Matplotlib, create simple animations, and embed MatplTable of ContentsChapter 1: Getting Started with Python and Jupyter Notebook Chapter Goal: Introduce the reader to the basics of Python programming language, philosophy, and installation. We will also learn how to install it on various platforms. This chapter also introduces the readers to Python programming with Jupyter notebook. In the end, we will also have a brief overview of the constituent libraries of SciPy stack. No of pages - 26 Sub -Topics • Python Programming Language • Installing Python on various platforms • Python Modes • Python IDEs • Scientific Python Ecosystem • Overview of Jupyter Notebook • Setting up Jupyter Notebook • Running Code in Jupyter Notebook Chapter 2: Getting Started with NumPy Chapter Goal: Get started with NumPy Ndarrays and basics of NumPy library. The chapter covers the instructions for installation and basic usage of NumPy. No of pages: 9 Sub - Topics: · Introduction to the NumPy Ndarrays · Ndarray Properties · NumPy Constants Chapter 3 : NumPy Routines and Getting started with Matplotlib Chapter goal – In this chapter, we will discuss the various Ndarray creation routines available in NumPy. We will also get started with visualizations with Matplotlib. We will learn how to visualize the various numerical ranges with Matplotlib. No of pages: 15 Sub - Topics: · Routines for creating Ndarrays · Matplotlib · Visualization with NumPy and Matplotlib Chapter 4 : Revisiting Matplotlib Visualizations Chapter goal – This chapter is focused on learning the details of Matplotlib styles for visualizing NumPy Ndarrays. No of pages: 24 Sub - Topics: • Single Line Plots • Multiline plots • Grid, Axes, and Labels • Colors, Lines, and Markers • Subplots • Object Oriented Style • Working with the text Chapter 5 : Styles and Layouts Chapter goal – This chapter is focused on learning the details of Matplotlib styles and layouts. No of pages: 12 Sub - Topics: 1. Styles 2. layouts Chapter 6 : Line, Bar, and Scatter Plots Chapter goal – In this chapter, we will learn how to create nice visualizations with lines, bars, and scatter. No of pages: 14 Sub - Topics: · Lines and Logs · Errorbar · Bar Graphs · Scatter Plot Chapter 7 : Histograms, Contours, and Streamplots Chapter goal – In this chapter, we will learn how to create nice visualizations histograms, contours, and streamplots. No of pages: 15 Sub - Topics: • Histograms • Contours • Plot vector entities with streamplots Chapter 8 : Image and Audio Visualization Chapter goal – Learn to work with Image Processing using NumPy and Matplotlib. Also learn how to process and visualize audio data as waveforms. No of pages: 15 Sub - Topics: • Visualizing images • Interpolation Methods • Audio Visualization • Audio Processing Chapter 9 : Pie and Polar Charts Chapter goal – Learn to work with Pie and Polar charts. No of pages: 12 Sub - Topics: 1. Pie charts 2. Polar charts Chapter 10 : PColor, Pcolormesh, and Colorbar Chapter goal – In this chapter, we will learn Pcolor, Pcolormesh, and colorbar. No of pages: 10 Sub - Topics: 1. PColor 2. Pcolormesh 3. Colorbar Chapter 11 : 3D Visualizations in Matplotlib Chapter goal – In this chapter, we will learn how to create 3D visualizations. No of pages: 17 Sub - Topics: • Getting Ready • Plotting 3D Line • 3D Scatter plot • 3D Contours • Wireframe, Surface, and Sample Data • Bar graphs • Quiver and Stemplot • 3D Volumes Chapter 12 : Animations with Matplotlib Chapter goal – In this chapter, we will learn how to create simple animations with Matplotlib. No of pages: 8 Sub - Topics: • Animation Basics • Celluloid library Chapter 13 : More Recipes of Visualizations with Matplotlib Chapter goal – In this chapter, we will learn more types of visualizations with Matplotlib. No of pages: 14 Sub - Topics: · Visualizing Function as an image and a contour · 3D Vignette · Decorated Scatter Plots · Time plots and Signals · Filled Plots · Step Plots · Hexbins · XKCD Style Chapter 14 : Introduction to Pandas Chapter goal – Get started with Pandas data structures No of pages: 10 Sub - Topics: • Introduction to Pandas • Series in Pandas • Dataframe in Pandas Chapter 15 : Data Acquisition Chapter goal – Read the data from various sources No of pages: 18 Sub - Topics: • Plain Text File Handling • Handling CSV with Python • Python and Excel • Writing and reading files with NumPy • Reading the data from a CSV file with NumPy • Matplotlib CBook • Reading data from a CSV • Reading data from an Excel • Reading data from JSON • Reading data from Pickle • Reading data from Web • Reading data from Relation databases • Reading Data from the clipboard Chapter 16 : Visualizing Data with Pandas and Matplotlib Chapter goal – Get started with Data Visualization with Matplotlib No of pages: 25 • Simple Plots • Bar Graphs • Histogram • Box Plot • Area Plots • Scatter Plot • Hexagonal Bin Plot • Pie Charts Chapter 17 : Introduction to Data Visualization with Seaborn Chapter goal – Get started with Pandas and seaborn No of pages: 20 Sub - Topics: • What is Seaborn? • Plotting statistical Relationships • Plotting Lines • Visualizing the distribution of data Chapter 18 : Visualizing real-life Data with Matplotlib and Seaborn Chapter goal – Get started with COVID and Animal disease datasets and Visualize them No of pages: 20 Sub - Topics: • COVID-19 Pandemic Data • Fetching the Pandemic Data Programmatically • Preparing the data for visualization • Visualization with Matplotlib and Seaborn • Visualization of Animal Disease Data

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Book SynopsisLearn how to harness modern deep-learning methods in many contexts. Packed with intuitive theory, practical implementation methods, and deep-learning case studies, this book reveals how to acquire the tools you need to design and implement like a deep-learning architect. It covers tools deep learning engineers can use in a wide range of fields, from biology to computer vision to business. With nine in-depth case studies, this book will ground you in creative, real-world deep learning thinking.You'll begin with a structured guide to using Keras, with helpful tips and best practices for making the most of the framework. Next, you'll learn how to train models effectively with transfer learning and self-supervised pre-training. You will then learn how to use a variety of model compressions for practical usage. Lastly, you will learn how to design successful neural network architectures and creatively reframe difficult problems into solvable ones. You'll learn notonly to understand and applTable of ContentsChapter 1: “A Deep Dive Into Keras”Chapter Goal: To give a structured yet deep overview of Keras and to lay the groundwork for implementations in future chapters.Number of Pages: ~30Subtopics1. Why Keras? Versatility and simplicity.2. Steps needed to create a Keras model: define architecture, compile, fit.a. Compile: discuss TensorFlow optimizers, losses, and metrics.b. Fit: discuss callbacks.3. Sequential model + example.4. Functional model + example.5. Visualizing Keras models.6. Data: using NumPy arrays, Keras Image Data Generator, and TensorFlow datasets.7. Hardware: using and accessing CPU, GPU, and TPU.Chapter 2: Pre-training Strategies and Transfer LearningChapter Goal: To understand the importance of transfer learning and to use a variety of transfer learning methods to solve deep learning problems efficiently.Number of Pages: ~30Subtopics1. Transfer learning theory, practical tips and tricks.2. Accessing and using Keras and TensorFlow pretrained models.a. Bonus: converting PyTorch models (PyTorch has a wider variety) into Keras models for greater access to pretrained networks.3. Manipulating pretrained models with other network elements.4. Layer freezing.5. Self-supervised learning methods.Chapter 3: “The Versatility of Autoencoders”Chapter Goal: To understand the versatility of autoencoders and to be able to use them in a wide variety of problem scenarios.Number of Pages: ~30Subtopics1. Autoencoder theory.2. One-dimensional data autoencoder implementation, tips and tricks.3. Convolutional autoencoder implementation, tips and tricks, special concerns.4. Using autoencoders for pretraining.a. Example case study: TabNet.5. Using autoencoders for feature reduction.6. Variational autoencoders for data generation.Chapter 4: “Model Compression for Practical Deployment”Chapter Goal: To understand pruning theory, implement pruning for effective model compression, and to recognize the important role of pruning in modern deep learning research.Number of Pages: ~20Subtopics1. Pruning theory.2. Pruning Keras models with TensorFlow.3. Exciting implications of pruning – the Lottery Ticket Hypothesis.a. Example case-study: no-training neural networks.b. Example case-study: extreme learning machines.Chapter 5: “Automating Model Design with Meta-Optimization”Chapter Goal: To understand what meta-optimization is and to be able to use it to effectively automate the design of neural networks.Number of Pages: ~20Subtopics1. Meta-optimization theory.2. Demonstration of meta-optimization using HyperOpt on Keras.3. Demonstration of Auto-ML and Neural Architecture Search.Chapter 6: “Successful Neural Network Architecture Design”Chapter Goal: To gain an understanding of principles in successful neural network architecture design through three case studies.Number of Pages: ~25Subtopics1. Diversity of neural network designs and the need to design specific architectures for particular problems.2. Theory and implementation of block/cell/module design and considerations.a. Example case study: Inception model.3. Theory and implementation of “Normal” and “extreme” usages of skip connections.a. Parallel towers and cardinalityb. Example case study: UMAP model.4. Neural network scaling.a. Example case study: EfficientNet.Chapter 7: “Reframing Difficult Deep Learning Problems”Chapter Goal: To explore how hard problems can be reframed to be solved by deep learning with three case studies.Number of Pages: ~30Subtopics1. The diversity of problems deep learning is being used to solve.2. Example case study: Siamese networks – experimenting with architecture.3. Example case study: DeepInsight – experimenting with data representation.4. Example case study: Semi-supervised generative adversarial networks – experimenting with data availability.

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Book SynopsisGet started quickly with IntelliJ, from installation to configuration to working with the source code and more. This tutorial will show you how to leverage IntelliJ's tools to develop clean, efficient Java applications. Author Ted Hagos will first walk you through buidling your first Java applications using IntelliJ. Then, he'll show you how to analyze your application, top to bottom; using version control and tools that allow you expand your application for big data or data science applications and more. You'll also learn some of the IDE's advanced features to fully maximize your application's capabilities.The last portion of the book focuses on application testing and deployment, and language- and framework- specific guidelines. After reading this book and working through its freely available source code, you'll be up to speed with this powerful IDE for today's Java development.What You Will LearnUse IntelliJ IDEA to build Java applicationsSet up your IDE and projectWork with sourcTable of Contents1. Install IntelliJ2. Getting Started3. Configuring the IDE4. Configuring Projects5. Working with Source Code6. Building Applications7. Analyzing Applications8. Version Control9. Big Data / Data Science Tools10. Other Tools11. Advanced IDE Features12. Migration Guides13. Language and Framework Specific Guidelines14. Testing 15. Deployment

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Book SynopsisDesign and build innovative, custom, data-driven Alexa skills for home or business. Working through several projects, this book teaches you how to build Alexa skills and integrate them with online APIs. If you have basic Python skills, this book will show you how to build data-driven Alexa skills. You will learn to use data to give your Alexa skills dynamic intelligence, in-depth knowledge, and the ability to remember. Data-Driven Alexa Skills takes a step-by-step approach to skill development. You will begin by configuring simple skills in the Alexa Skill Builder Console. Then you will develop advanced custom skills that use several Alexa Skill Development Kit features to integrate with lambda functions, Amazon Web Services (AWS), and Internet data feeds. These advanced skills enable you to link user accounts, query and store data using a NoSQL database, and access real estate listings and stock prices via web APIs. What You Will LearnSet up and configure your development environmTable of ContentsPart I: Getting Started Chapter 1: Voice User Interfaces Chapter 2: Routines and Blueprints Chapter 3: The Developer Accounts Chapter 4: Creating the VUI for a Custom Data-driven Skill Chapter 5: Writing the Back-end Code Chapter 6: Publishing an Alexa Skill Part II: Custom Skill Development Chapter 7: Custom Alexa Skills Chapter 8: Beyond Hello World Chapter 9: Configuring the VUI Chapter 10: Using APL to Present on Screens Chapter 11: Coding the Lambda Function Chapter 12: Unit Testing an Alexa Skill Chapter 13: Storing the Data Part III: Using APIs in Advanced Skills Chapter 14: A Personal Net Worth Skill Chapter 15: The Real Estate API Chapter 16: The Stock Market API Chapter 17: What’s Next?

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Book SynopsisDesign and develop Java-based RESTful APIs using the latest versions of the Spring MVC and Spring Boot frameworks. This book walks you through the process of designing and building a REST application while delving into design principles and best practices for versioning, security, documentation, error handling, paging, and sorting. Spring RESTprovides a brief introduction to REST, HTTP, and web infrastructure. You will learn about several Spring projects such as Spring Boot, Spring MVC, Spring Data JPA, and Spring Security, and the role they play in simplifying REST application development. You will learn how to build clients that consume REST services. Finally, you will learn how to use the Spring MVC test framework to unit test and integration test your REST API. After reading this book, you will come away with all the skills to build sophisticated REST applications using Spring technologies. What You Will LearnBuild Java-based microservices, native cloud, or any applications uTable of Contents1. Introduction to REST2. Spring MVC & Spring Boot Primer3. RESTful Spring4. Beginning the QuickPoll Application5. Error Handling6. Documenting REST Services7. Versioning, Paging, and Sorting8. Security9. Clients and Testing10. HATEOASA. Installing cURL on Windows

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Book SynopsisGet started quickly with Qt, the popular open source C++ framework for building C++-based applications and games. This book will have you building both fully functional desktop and mobile applications in no time, including some simple game applications.  Introducing Qt 6 begins by guiding you in setting up your tools and environment, and then walks you through the first baby steps of Qt framework. Next, you''ll learn the basics of how project and app structure are set up using Qt. Then, you''ll begin your first real hands-on projects using Qt, including a task and problem management application and two games.  As you progress, you can enhance these apps and games using additional Qt components and features. The book then delves into advanced topics in Qt, learning above and beyond what the Qt docs can offer, including local storage, C++ integration, deployment to Windows and Android, custom components and how to work with them. UpTable of ContentsPart 11. IntroductionPart 2 - Content2. Setting up the Tools 3. First Baby Steps with Qt4. Explaining the Basics of Project and App Structure5. First Real Projects6. Taskmaster7. Hang-Man Game8. Rock, Paper, Scissors GamePart 3 - Components, Features and Things9. Components10. Features 11. Writing Diagrams in Qt12. Advanced Topics in Qt

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Book SynopsisQuickly find solutions to dozens of common programming problemsencountered while building Java applications, with recipes presented in the popular problem-solution format. Look up the programming problem that you want to resolve. Read the solution. Apply the solution directly in your own code. Problem solved!Java 17 Recipes is updated to reflect changes in specification and implementation since the Java 9 edition of this book. Java 17 is the next long-term support release (LTS) of the core Java Standard Edition (SE) version 17 which also includes some of the features from previous short term support (STS) releases of Java 16 and previous versions.This new edition covers of some of the newest features, APIs, and more such as pattern matching for switch, Restore Always-Strict-Floating-Point-Semantics, enhanced pseudo-random number generators, the vector API, sealed classes, and enhancements in the use of String. Source code for all recipes is available in a dedicated GitHub repository. Table of Contents1. Getting Started with Java 172. Java 17 Enhancements3. Strings4. Numbers and Dates5. Object-Oriented Java6. Lambda Expressions7. Data Structures and Collections8. Input and Output9. Exceptions and Logging10. Concurrency11. Debugging and Unit Testing12. Unicode, Internationalization, and Currency Codes13. Working with Databases14. JavaFX Fundamentals15. Graphics with JavaFX16. Media with JavaFX17. Java Web Applications18. Nashorn and Scripting19. E-mail20. JSON and XML Processing21. Networking22. Java Modularity

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Book SynopsisDiscover what's new in C# and .NET for Windows programming. This book isa condensed code and syntax reference to the C# programming language, updated with the latest features of version 10 for .NET 6.You'll review the essential C# 10 and earlier syntax, not previously covered, in a well-organized format that can be used as a handy reference. Specifically, unions, generic attributes, CallerArgumentExpression, params span, Records,Init only setters,Top-level statements,Pattern matching enhancements,Native sized integers,Function pointers and more. You'll find a concise reference to the C# language syntax: short, simple, and focused code examples; a well laid out table of contents; and a comprehensive index allowing easy review. You won't find any technical jargon, bloated samples, drawn-out history lessons, or witty stories. What you will find is a language reference that is to the point and highly accessible.The book is a must-have for any C# programmer. What You Will LearnEmploy nuTable of Contents1. Hello World2. Compile and Run3. Variables4. Operators5. Strings6. Arrays7. Conditionals8. Loops9. Methods10. Class11. Inheritance12. Redefining Members13. Access Levels14. Static15. Properties16. Indexers17. Interfaces18. Abstract19. Namespaces20. Enum21. Exception Handling22. Operator Overloading23. Custom Conversions24. Struct25. Preprocessors26. Delegates27. Events28. Generics29. Constants30. Asynchronous Methods

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Book SynopsisTake a problem-solution approach for programming enterprise Java or Java EE applications and microservices for cloud-based solutions, enterprise database applications, and even small business web applications. Java EE to Jakarta EE 10 Recipes provides effective, practical, and proven code snippets that you can immediately use to accomplish just about any task that you may encounter. You can feel confident using the reliable solutions that are demonstrated in this book in your personal or corporate environment. Java EE was made open source under the Eclipse Foundation, and Jakarta EE is the new name for what used to be termed the Java Platform, Enterprise Edition. This book helps you rejuvenate your Java expertise and put the platform''s latest capabilities to use for quickly developing robust applications. If you are new to Jakarta EE, this book will help you learn the features of the platform and benefit from one of the most widely used anTable of ContentsRevision Notes from AuthorBased on the book preview, below is the list of changes/updates I see relevant for the next revisions of this book - Jakarta EE 10 Recipes. ----------------------------------Introduction: Adding following details for Jakarta EE 9, 9.1 and 10 releases like timelines and theme. Updating instructions for enabling Jakarta EE support using NetBeans IDE Adding instructions for using the following IDE for building Jakarta EE applications. Eclipse IDE Visual Studio Code Providing instructions for installing the following build tools for Jakarta EE Maven Gradle All Chapters: Rename to chapter title Servlets and Jakarta Server Pages Updating terminologies as below: JavaServer Pages(JSP) to Jakarta Server Pages JavaServer Faces(JSF) to Jakarta Faces, JavaMail to Jakarta Mail … Reworking and verifying the code examples by performing the following modifications Updating import statements in all code samples from javax.* to jakarta.*. Updating code to make use of the latest Java 11 features Updating instructions for enabling Jakarta EE support using NetBeans IDE Adding instructions for using the following IDE as well for building Jakarta EE applications. Eclipse IDE Visual Studio Code Ensuring the code can be executed in the latest releases of the following servers Open Liberty, WildFly and GlassFish servers as are the flag bearers for Jakarta EE compliance. Also verifying the code with other servers based on their availability with support for latest functionality like Payara or Apache TomEE or Oracle Weblogic and provide any specific instructions in case applicable. Note: As I would dig deeper into the content will be able to suggest more pointsChapter 1: Servlets and JavaServer Pages Rename to chapter title Servlets 1-8 (Adding content about - Improving performance with server push) Adding a recipe for adding a user authentication to Servlets. Adding a recipe for defining servlet behaviour based on user authorization Adding more recipes for the following topics session tracking and Handling cookies HTTP Session handling Servlet Filters / URL Redirections . Move JSP related recipes to a separate chapter New Chapter 2: Creating a New Chapter on Jakarta Server Pages Adding recipes for the following topics Handling file uploads Add recipe for Implementing Internationalization Managed Bean 2.0 Chapter 2: JavaServer Faces Fundamentals Rename to chapter title Jakarta Faces Fundamentals Updating code as per Jakarta Faces 4.0 specification Adding recipes for the following topics Externalizing strings using resource bundles Chapter 3: Advanced JavaServer Faces Rename to chapter title Advanced Jakarta Faces Chapter 4: Eclipse Krazo renaming it to Jakarta MVC and updating it with content for Jakarta MVC. Chapter 5: JDBC with Jakarta EE Adding a Chapter with recipes on Jakarta Transactions Chapter 6: Object-Relational Mapping Adding sections about migrating from Hibernate ORM 5.5 to Jakarta Persistence. Chapter 7: Jakarta NoSQL Add recipes related to working with graph databases (like neo4j). Chapter 8: Enterprise JavaBeans Updating recipes as Jakarta Enterprise Beans 4.0 specifications. Chapter 9: Java Persistence Query Language Rename chapter title to Jakarta Persistence Query Language Chapter 10: Bean Validation Updating recipes as per Jakarta Bean validation 3.0 standard Adding recipes about Migration from Hibernate Validator Chapter 11: Contexts and Dependency Injection Updates based on the latest CDI support Adding recipes related to Interceptor bindings Decorators Firing Events Chapter 12: Java Message Service Rename to Jakarta Messaging Updating recipes as per Jakarta Messaging 3.0Chapter 13: RESTful Web ServicesAdding a Chapter on migrating from Spring would also make sense as the upcoming release of Spring Framework 6 will align with Jakarta EE starting Q3 2021Add recipes for Handling various status codes in HTTP responses. Chapter 14: WebSockets and JSONContent on WebSockets would be better suited after chapter Restructuring sections on Web Sockets from Chapter 14 to Chapter 2 after HTTP/2 Server Push in Servlets changes Adding a chapter on Jakarta Concurrency Chapter 15: SecurityRename to Jakarta SecurityAdding recipes for The authentication mechanism for Client-Cert and Digest Support for OpenID, OAuth and JWT Chapter 16: Concurrency and Batch Updating recipes as per Jakarta Concurrency 3.0 Chapter 17: Deploying to Containers Adding topics related to running microservices targeted at smaller runtimes as per Jakarta Core Profile standard 1. Servlets and Java Server Pages2. JavaServer Faces Fundamentals3. Advanced JavaServer Faces4. Eclipse Krazo5. JDBC With Jakarta EE6. Object-Relational Mapping7. Jakarta NoSQL8. Enterprise JavaBeans9. Java Persistence Query Language10. Bean Validation11. Contexts and Dependency Injection12. Java Message Service13. RESTful Web Services14. WebSockets and JSON15. Security16. Concurrency and Batch17. Deploying to Containers

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Book SynopsisRapidly prototype and program new IoT and Edge solutions using low-cost Maker tech, such as those from Arduino, Raspberry Pi and Nvidia. With a focus on the electronics, this book allows experienced computer science students as well as researchers, practitioners, and even hobbyists to dive right into actual engineering of prototypes and not just theoretical programming and algorithms. You''ll learn to interface sensors, work with various communication mediums, incorporate wired and wireless communication protocols, and more with these single board computers. All while working in the popular Python programming language. Additionally, you''ll discover both scripting-based and drag and drop solutions for different problems. As well as a variety of useful, data gathering approaches.  Then you can apply what you''ve learned to IoT projects and troubleshooting Industry 4.0 problems. The rapid growth of technology and new development Table of ContentsInternet of Things Using Single Board ComputersChapter 1: An Overview of IoT Chapter 2: IoT Architecture Chapter 3: Programming Through python Chapter 4: Wireless Technology for IoT Chapter 5: Building IoT with Raspberry Pi Chapter 6: Home Automation Chapter 7: Smart Cities and Smart Grids Chapter 8: Electric Vehicle Charging Chapter 9: Agriculture

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Book SynopsisTransition from a back-end developer to a full-stack developer with knowledge of all the dimensions of web application development, namely, front-end, back-end and server-side software. This book provides a comprehensive overview of Streamlit, allowing developers and programmers of all backgrounds to get up to speed in as little time as possible. Streamlit is a pure Python web framework that will bridge the skills gap and shorten development time from weeks to hours. This book walks you through the complete cycle of web application development, from an introductory to advanced level with accompanying source code and resources. You will be exposed to developing basic, intermediate, and sophisticated user interfaces and subsequently you will be acquainted with data visualization, database systems, application security, and cloud deployment in Streamlit.  In a market with a surplus demand for full stack developers, this skill set could not possiblTable of Contents Part I: Introduction to Streamlit 1 Getting Started with Streamlit 1.1 Why Streamlit? 1.2 How Streamlit Works 1.3 Firing it up 2 Streamlit Basics 2.1 The Streamlit API 2.2 Creating a basic app Part II: Developing Advanced Interfaces and Applications 3 Architecting Streamlit’s Front-end Design 3.1 Designing the application 3.2 Provisioning multi-page applications 3.3 Data wrangling 4 Graphing in Depth 4.1 Visualization stack 4.2 Exploring Plotly data visualizations Part III: Interfacing with Database and Back-end Systems 5 Database Integration 5.1 Relational Databases 5.2 Non-relational databases 6 Back-end Servers 6.1 The need for back-end servers 6.2 Front-end/ Back-end Communication 6.3 Working with JSON files 6.4 Provisioning a back-end server 6.5 Multi-threading and multi-processing request 6.6 Connecting Streamlit to a Back-end Server Part IV: Enforcing Application Security and Privacy 7 Session State 7.1 Introducing session IDs 7.2 Implementing session state persistently 7.3 Recording user insights 7.4 Implementing session state natively 7.5 Cookies management 8 Authentication and Application Security 8.1 Developing user accounts 8.2 Verifying user credentials 8.3 Secrets management 8.4 Anti-SQL injection measures with SQL Alchemy 8.5 Configuring Git Ignore variables Part V: Deploying Streamlit to the Cloud 9 Persistent Deployment 9.1 Deployment to Streamlit Sharing 9.2 Deployment to Linux 9.3 Deployment to Windows Server 10 Exposing Local Streamlit to the World Wide Web 10.1 Port forwarding over network gateway 10.2 Reverse Port Forwarding using NGROK Part VI: Streamlit Custom Components 11 Building Streamlit components with React.js 11.1 Introduction to Streamlit custom components 11.2 Using React.js to create custom HTML components 11.3 Deploying components as a Pip package 12 Extra-Streamlit-Components Package 12.1 Stepper bar 12.2 Splash screen . . 12.3 Tab bar 12.4 Cookie Manager Part VII: Streamlit Case Studies 13 General Use Cases 13.1 Data science & machine learning applications 13.2 Dashboards and real-time applications 13.3 Time-series applications 13.4 Advanced application development 14 Steamlit at Work 14.1 Iberdrola Renewables 14.2 DummyLearn.com

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Book SynopsisOptimize, develop, and design PyTorch and TensorFlow models for a specific problem using the Microsoft Neural Network Intelligence (NNI) toolkit. This book includes practical examples illustrating automated deep learning approaches and provides techniques to facilitate your deep learning model development. The first chapters of this book cover the basics of NNI toolkit usage and methods for solving hyper-parameter optimization tasks. You will understand the black-box function maximization problem using NNI, and know how to prepare a TensorFlow or PyTorch model for hyper-parameter tuning, launch an experiment, and interpret the results. The book dives into optimization tuners and the search algorithms they are based on: Evolution search, Annealing search, and the Bayesian Optimization approach. The Neural Architecture Search is covered and you will learn how to develop deep learning models from scratch. Multi-trial and one-shot searching approaches of automatic neural networTable of ContentsChapter 1: Introduction to Neural Network Intelligence1.1 Installation1.2 Trial, search space, experiment1.3 Finding maxima of multivariate function1.4 Interacting with NNIChapter 2:Hyper-Parameter Tuning2.1 Preparing a model for hyper-parameter tuning2.2 Running experiment2.3 Interpreting results2.4 DebuggingChapter 3: Hyper-Parameter TunersChapter 4: Neural Architecture Search: Multi-trial4.1 Constructing a search space4.2 Running architecture search4.3 Exploration strategies4.4 Comparing exploration strategiesChapter 5: Neural Architecture Search: One-shot5.1 What is one-shot NAS?5.2 ENAS5.3 DARTSChapter 6: Model Compression6.1 What is model compression?6.2 Compressing your model6.3 Pruning6.4 QuantizationChapter 7: Advanced NNI

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Book SynopsisExplore the world of programming languages through Python and learn the building blocks of writing programs. This book covers Python 3.10, explaining it through six key concepts. Each chapter contains a real-world example with practical advice and a section on advanced concepts. You''ll start by reviewing the concept of expressions and functions, which are two of the core building blocks of programming languages.  You''ll then move on to object-oriented concepts to help gain a practical understanding of Python, along with a chapter on control flow constructs. The book also takes a close look at sequences, explaining constructs and additional types, and wraps up with a chapter on modules, focusing on how to use and create packages. Whether you''re new to programming or already an experienced developer, upon finishing this book, you will have a solid understanding of Python''s state-of-the-art development features.WhatTable of ContentsChapter 1: Expression- Explains expression as the first key concept of a programming language.1.1. What is an expression?1.2. Expressions containing different types1.3. Variable names1.4. Statements1.5. Deleting variable names1.6. Further language constructs1.7. Expressions and statements in practice1.8. References:Chapter 2: Function- Explains function as the second most important building block.2.1. What is a function?2.2. Calling functions2.3. Functions with side effects2.4. Function parameters2.5. Defining functions2.6. Referencing to variable and function names2.7. Function as parameter2.8. Embedded function definitions2.9. Function in practice2.10. ReferencesChapter 3: Class- Explains object-oriented concepts as everything is an object in Python.3.1. What is object-oriented programming?3.2. What is a class?3.3. Creating objects3.4. Using attributes and methods3.5. Defining classes3.6. Connection between classes3.7. Properties3.8. Inheritance3.9. Embedded classes3.10. Special methods3.11. Classes in practice3.12. ReferencesChapter 4. Control flow- Explains what control flow constructs and everything related to it (exceptions, recursion, etc.)4.1. What is control flow?4.2. Conditional statement4.3. Condition-controlled loops4.4. Count-controlled loops4.5. Exception handling4.6. Context management4.7. ReferencesChapter 5: Sequence- Explains the list like constructs and additional container types.5.1. What is a sequence?5.2. List and its operations5.3. List comprehension5.4. Tuples 5.5. Dictionaries5.6. Sets5.7. ReferencesChapter 6: Module- Explains how to use and create packages.6.1. What is a module?6.2. Built-in modules6.3. Create your own modules6.4. Packages6.5. Package management6.6. Interesting third party packages6.7. References

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Book SynopsisUnderstand the theory and implementation of simulation. This book covers simulation topics from a scenario-driven approach using Python and rich visualizations and tabulations. The book discusses simulation used in the natural and social sciences and with simulations taken from the top algorithms used in the industry today. The authors use an engaging approach that mixes mathematics and programming experiments with beginning-intermediate level Python code to create an immersive learning experience that is cohesive and integrated. After reading this book, you will have an understanding of simulation used in natural sciences, engineering, and social sciences using Python.What You''ll Learn Use Python and numerical computation to demonstrate the power of simulation Choose a paradigm to run a simulation Draw statistical inTable of ContentsChapter 1: Calculating Pi and Beyond: Searching Order in Disorder with Simulation [30]Description: The beginning chapter will use Monte Carlo simulation as a topic to introduce some fundamental concepts in simulation.Topics to be covered: 1. Simulating Pi2. The goat problem and uniform sampling3. How to properly set a simulation environment Chapter 2: Markov Chain: A Peek into the Future [20]Description: Markov chain simulation will be introduced from both probabilistic perspective and matrix multiplication perspective.Topics to be covered: 1. How to predict weather?2. The transition matrix and stability states3. Markov chain Monte Carlo simulation Chapter 3: Multi-Armed Bandits: Probability Simulation and Bayesian Statistics [30]Description: Classical multi-armed bandits’ model will be introduced to continue the probabilistic perspective of the previous chapter. In addition, Bayesian statistics will be introduced.Topics to be covered: 1. Introduction to multi-armed bandit2. Greedy versus explorative strategies3. The interpretation of a Bayesian statistician. Chapter 4: Balls in 2D Box: A Simplest Physics Engine [20]Description: This chapter is mainly about event-driven simulation. It is not about simulation in the time space but in the event space.Topics to be covered: 1. Introduce the physics laws that govern motion2. Use event-driven paradigm to build a physics engine3. More realistic simulation with friction Chapter 5: Percolation: Threshold and Phase Change [25]Description: Phase changing is an important physics behavior for systems near critical boundaries. We are going to simulate critical behaviors using percolation as examples.Topics to be covered: 1. The concept of percolation and 2. Why dimension matters: 1D percolation and 2D percolation3. 3D percolation and even higher dimensionsChapter 6: Queuing System: How Stock Trades are Made [30]Description: As the first example in the business world, concepts in queuing systems are introduced and the simulation using basic data structures like queue and deque will be carried out.Topics to be covered: 1. Basic data structures in Python2. Microstructure of trading3. Simulating trading Chapter 7: Rock, Scissor and Paper: Multi-Agent Simulation [30]Description: Sometimes we want to simulate a system with multiple agents acting on their own behalf. In this chapter, we are going to run a multi-agent simulation and test the performance of different competing strategies in such a scenario.Topics to be covered: 1. Characteristics of multi-agent system2. Baseline strategies3. Analyzing nontrivial strategiesChapter 8: Matthew Effect and Tax Policy: Why the Rich Keeps Getting Richer[30]Description: Differential equation is an important field of study that governs a big group of phenomena. In this chapter, we are going to study it with a very relevant topic: wealth distribution in modern society. Topics to be covered: 1. Introduction of differential equations2. Matthew effect and ROI3. How tax policy can gauge social wealth distribution Chapter 9: Misinformation Spreading: Simulation on a Graph (Centrality, Networkx)[30]Description: Network simulation is another important domain. Nowadays social media like Twitter, Facebook and reddit can be easily modelled as a network. We will cover a simple simulation to study how misinformation can spread in a network and how we can fight against it.Topics to be covered: 1. Concepts of a network2. Simulate misinformation spreading in a directed network3. How to fight misinformation (or suppress freedom of expression)Chapter 10: Simulated Annealing and Genetic Algorithm [30]Description: There are two simulation algorithms widely used in research and industry that mimic natural phenomena. We are going to use them to solve two real world problems and explain the origin of their power.Topics to be covered: 4. Simulated Annealing Basics5. Use Simulated Annealing to solve an optimization problem6. Genetic Algorithm7. Use Genetic algorithm to solve an optimization problem

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Book SynopsisProgramming permeates almost all aspects of our lives. This includes being active on social media, shopping online, and participating in virtual courses. It also includes driving a car and using many devices.  This book will teach you the basics of programming using the Processing programming language and provide practice with logical, algorithmic thinking.  It can provide insight into what is involved in producing the technical infrastructure of our world. While reading this book, you can build programs based on your own ideas, using images you create or acquire and making connections to activities you enjoy.The chapters in the book will demonstrate the process of programming, starting with formulating an idea, planning, building on past projects, and refining the work, similar to writing an essay or composing a song. This approach will guide you to make use of logic and mathematics to produce beautiful effects. The text contains an Appendix with an introductiTable of Contents1. Basics2. Interactions3. Animation Using Arrays and Parallel Structures4. Classes 5. More Interactions6. Images, Graphics, and Building on Prior Work7. Using Files for Making a Holiday Card8. Combining Videos, Images, and Graphics9. Hangman10. 3DAppendix: Processing and JavaScript: p5.js

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Book SynopsisGet started with artificial intelligence for medical sciences and psychology. This book will help healthcare professionals and technologists solve problems using machine learning methods, computer vision, and natural language processing (NLP) techniques. The book covers ways to use neural networks to classify patients with diseases. You will know how to apply computer vision techniques and convolutional neural networks (CNNs) to segment diseases such as cancer (e.g., skin, breast, and brain cancer) and pneumonia. The hidden Markov decision making process is presented to help you identify hidden states of time-dependent data. In addition, it shows how NLP techniques are used in medical records classification. This book is suitable for experienced practitioners in varying medical specialties (neurology, virology, radiology, oncology, and more) who want to learn Python programming to help them work efficiently. It is also intended for data scientists, machine leTable of ContentsChapter 1: An Introduction to Artificial Intelligence for Medical SciencesChapter goal: This is the initial chapter. Subsequently, it encapsulates the specific context and structure of the book. Then, it states the varying medical specialties central to this book. Likewise, it properly presents independent subsets of artificial intelligence. Besides that, it unveils valuable tools for undertaking exercises; Python programming language, distribution package, and libraries. Afterward, it sufficiently acquaints you with different algorithms, including when to carry them out.Sub-topics:● Context of the book.● The book’s central point.● Artificial Intelligence subsets covered in this book.● Structure of the book.● Tools that this book implements.○ Python distribution package.○ Anaconda distribution package.○ Jupyter Notebook.○ Python libraries.● Encapsulating Artificial Intelligence.● Debunking algorithms.● Debunking supervised algorithms.● Debunking unsupervised algorithms.● Debunking Artificial Neural Networks.Chapter 2: Realizing Patterns in Common Diseases with Neural NetworksChapter goal: This chapter purportedly contains the application of artificial neural networks in modelling medical data. It properly instigates deep belief networks to model data and predicts whether a patient suffers from an ordinary disease (i.e., pneumonia and diabetes). Equally, it appraises the networks with fundamental metrics to discern the magnitude to which the networks set apart patients who suffer from the disease from those who do not.Sub-topics:● Classifying patients’ Cardiovascular disease diagnosis outcome data by executing a deepbelief network.● Preprocessing the Cardiovascular disease diagnosis outcome data.● Debunking deep belief networks.o Designing the deep belief network.o Relu Activation function.o Sigmoid activation function.● Training the deep belief network.● Outlining the deep belief networks predictions.● Considering the deep belief network’s performance.● Classifying patients’ diabetes diagnosis outcome data by executing a deep belief network.● Outlining the deep belief networks predictions .● Considering the deep belief network’s performance.● Conclusion.Chapter 3: A Case for COVID-19 Identifying Hidden States and Simulation ResultsChapter goal: This chapter instigates a set of series analysis methods to uniquely discern patterns in the US COVID-19 confirmed cases. To begin with, the Gaussian Hidden Markov Model inherits the series data, models it and identifies the hidden states, including the means and covariance in those states. Subsequently, the Monte Carlo simulation method replicates US COVID-19 confirmed cases across multiple trials, thus providing us with a rich comprehending of the patternChapter content:● Debunking the Hidden Markov Model● Descriptive analysis● Carrying Out the Gaussian Hidden Markov Modelo Considering the Hidden States in US COVID-19 Confirmed Cases with the GaussianHidden Markov Model● Simulating US COVID-19 Confirmed Cases with the Monte Carlo Simulation Methodo US COVID-19 confirmed cases simulation results● ConclusionChapter 4: Cancer Segmentation with Neural NetworksChapter goal: This chapter typically exhibits the practical application of computer vision andconvolutional neural networks for breast and skin Cancer realization and segmentation. Equally, it shows an approach to filter medical scans by applying canny, luplican, and sobel filters. It concludes by ascertaining the extent to which the networks accurately differentiate scans of patients with and without Cancer.Chapter content:● Debunking Cancer.● Debunking Skin Cancer● Depicting scans of a patient with Skin Cancer.● Classifying Patients’ Skin Cancer Diagnosis Image Data by Executing a Convolutional Neural Network.o Preprocessing the training Skin Cancer Image Data.o Preprocessing the Validation Skin Cancer Image Data.o Generating the Training Skin Cancer Diagnosis Image Data.o Tuning the Training Skin Cancer Image Data.o Executing the Convolutional Neural Network to Classify Patients’ Skin CancerDiagnosis Image Data.o Considering the Convolutional Neural Network’s Performance.o Debunking Breast Cancer.● Classifying Ultrasound Scans of Breast Cancer Patients by Executing a Convolutional Neural Network.o Preprocessing the Validation Breast Cancer Image Data .o Preprocessing the Validation Breast Cancer Image Data .o Generating the Training Breast Cancer Diagnosis Image Data.o Tuning the Training Breast Cancer Image Data.o Executing the Convolutional Neural Network to Classify Patients’ Breast CancerDiagnosis Image Data.o Considering the Convolutional Neural Network’s Performance.● Conclusion.Chapter 5: Modelling Magnetic Resonance Imaging and X-Rays by Carrying out Artificial Neural NetworksChapter goal: This chapter intimately acquaints you with the practical application of computer vision and artificial neural networks in neurology and radiology. It promptly carries out convolutional neural networks for image classification. The initial network models MRI scans to set apart patients with and without a brain tumor, and the second network models X-ray scans to set apart patients with and without pneumonia. Besides that, it unveils an effective technique for appraising networks in medical image classification.Sub-topics:● Debunking Brain Tumors.● Classifying Patients’ Model Magnetic Resonance Imaging (MRI) Data by Executing aConvolutional Neural Network.o Depicting MRI Scan of Patients with a Brain Tumor.o Depicting Brain Scans without a Brain Tumor.o Preprocessing the Training MRI Image Data.o Preprocessing the Validation MRI Image Data.o Generating the Training MRI Image Data.o Tuning the Training MRI Image Data.o Executing the Convolutional Neural Network to Classify Patients’ MRI Image Data.o Considering the Convolutional Neural Network’s Performance.● Debunking Pneumonia.o Classifying Patients’ CT scan Data by Executing a Convolutional Neural Network.o Depicting an X-Ray scan of a Patient with Pneumonia.o Depicting an X-Ray scan of a Patient without Pneumonia.o Processing the X-Ray Image Data.o Generating the Training Chest X-Ray Image Data.o Preprocessing the Validation Chest X-Ray Image Data.o Generating the Validation Chest X-Ray Image Data.o Tuning the Training Chest X-Ray Image Data.o Executing the Convolutional Neural Network to Classify Patients’ Chest X-Ray ImageData.▪ Considering the Convolutional Neural Network’s Performance.● Conclusion.Chapter 6: A Case for COVID-19 CT Scan SegmentationChapter goal: This chapter presents an approach for carrying out convolutional neural networks to model chest CT scan images and differentiate between patients with and without COVID-19.Sub-topics:● Classifying Patients’ Model Magnetic Resonance Imaging (MRI) Data by Carrying out aConvolutional Neural Network.o Depicting a Chest CT scan of a COVID-19 Negative Patient.o Depicting a CT scan of COVID-19 Negative Patient.o Preprocessing the Training COVID-19 Data.o Preprocessing the Validation COVID-19 CT Scan Data.o Generating the Training COVID-19 CT Scan Data.o Tuning the Training COVID-19 CT Scan Data.● Data.o Considering the Convolutional Neural Network’s Performance.● Conclusion.Chapter 7 Modelling Clinical Trial DataChapter goal: This chapter familiarizes you with the prime essentials of the most widespread method for adequately investigating data from a clinical trial, recognized as a survival method. It debunks the Nelson-Aalen additive model. To begin with, it encapsulates the method. Subsequently, it promptly presents exploratory analysis, then correlation analysis by carrying out the Pearson correlation method. Following that, it outlines the survival table, then fits the model. It concludes by carefully outlining the profile table, confidence interval, and reproducing the cumulative and baseline hazard.sub-topics:● Debunking Clinical Trials.● An Overview of Survival Analysis.● Context of the Chapter.● Exploring the Nelson-Aalen Additive Model.● Descriptive Analysis.● Realizing a Correlation Relationship.● Outlining the Survival Table.● Carrying out the Nelson-Aalen Additive Model.o Outlining the Nelson-Aalen additive Model’s Confidence Intervalo Discerning the Survival Hazard.o Discerning the Cumulative Survival Hazard.o Baseline Survival Hazard.● Conclusion.● References.Chapter 8: Medical Record CategorizationChapter goal: This chapter sufficiently apprises a wholesome approach for realizing patterns in medical records by carrying out a linear discriminant analysis model. To begin with, it summarizes medical recording. Subsequently, it exhibits a technique of cleansing textual data by carrying out fundamental methods like regularization and TfidfVectorizer. Afterward, it executes the method to classify the medical specialty, then it assesses the extent to which it segregates classes.Sub-topics:● Medical Records.● Context of Chapter.● Debunking Categorization with Linear Discriminant Analysis.o Descriptive Statistics.o Preprocessing the Medical Records Data.o Carrying out Regular Expression.o Carrying Out Word Vectorization.o Carrying out the Linear Discriminant Analysis Model to Classify Patients’ MedicalRecords.o Considering the Linear Discriminant Analysis Model’s Performance.● Conclusion.Chapter 9: A Case for Psychology: Factoring and Clustering Personality DimensionsChapter goal: This chapter introduces you to analyzing the underlying patterns in human behavior by promptly carrying out exploratory factor analysis and cluster analysis. To begin with, it covers the big five personality dimensions. Following that, it presents an approach for typically collecting data by retaining a Likert scale and measuring the reliability of the scale with Cronbach’s reliability testing strategy. Subsequently, it performs factor analysis; beginning with estimating Bartlett Sphericity statistics, then the Kaiser-Meyer-Olkin statistic. Following that, it rotates the eigenvalues by carrying out the varimax rotation method and estimates the proportional variances and cumulative variances. In addition, it executes the K-Means method to observe clusters in the data; beginning with standardizing the data and carrying out principal component analysis.Sub-topics:● Debunking Personality Dimensions.● Questionnaires.● Likert Scale.● Reliability.o Spearman-Brown Reliability Testing Strategy.o Carrying out Cronbach's Reliability Testing Strategy.● Carrying out Factor Model.o Carrying out the Bartlett Sphericity Test.o Carrying out the Kaiser-Meyer-Olkin Test.o Discerning K with a Scree Plot.o Carrying out Eigenvalue Rotation.▪ Varimax Rotation.● Carrying out Cluster Analysis.o Carrying out Principal Component Analysis.O Returning K-Means label.

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Book SynopsisData is the indispensable fuel that drives the decision making of everything from governments, to major corporations, to sports teams. Its value is almost beyond measure. But what if that data is either unavailable or problematic to access? That''s where synthetic data comes in. This book will show you how to generate synthetic data and use it to maximum effect.Synthetic Data for Deep Learning begins by tracing the need for and development of synthetic data before delving into the role it plays in machine learning and computer vision. You''ll gain insight into how synthetic data can be used to study the benefits of autonomous driving systems and to make accurate predictions about real-world data. You''ll work through practical examples of synthetic data generation using Python and R, placing its purpose and methods in a real-world context. Generative Adversarial Networks (GANs) are also covered in detail, explaining how they work and their potential applicationsTable of ContentsChapter I: Introduction to Data 40 pagesChapter Goal: The book section entitled "Data" aims to provide readers with information on the history, definition, and future of data storage, as well as the role that synthetic data can play in the field of computer vision. 1.1. The History of Data1.3. Definitions of Synthetic Data1.4. The Lifecycle of Data1.5. The Future of Data Storage1.6. Synthetic Data and Metaverse1.7. Computer Vision1.8. Generating an Artificial Neural Network Using Package “nnet” in R1.9. Understanding of Visual Scenes1.10. Segmentation Problem1.11. Accuracy Problems1.12. Generative Pre-trained Transformer 3 (GPT-3) Chapter 2: Synthetic Data 40 pagesChapter Goal: The purpose of this chapter is to provide information about synthetic data and how it can be used to benefit autonomous driving systems. Synthetic data is a term used to describe data that has been generated by a computer. 2.1. Synthetic Data2.2. A Brief History of Synthetic Data2.3. Types of Synthetic Data2.4. Benefits and Challenges of Synthetic Data2.5. Generating Synthetic Data in A Simple Way2.6. An Example of Biased Synthetic Data Generation2.7. Domain Transfer2.8. Domain Adaptation2.9. Domain Randomization2.10. Using Video Games to Create Synthetic Data2.11. Synthetic Data And Autonomous Driving System2.11.1. Perception2.11.2. Localization2.11.3. Prediction2.11.4. Decision Making2.12. Simulation in Autonomous Vehicle Companies2.13. How to Make Automatic Data Labeling? 2.14. Is Real-World Experience Unavoidable? 2.15. Data for Learning Medical Images2.16. Reinforcement Learning2.17. Self-Supervised LearningChapter 3: Synthetic Data Generation with R..... 55 pagesChapter Goal: The purpose of this book section is to provide information about the content and purpose of synthetic data generation with R. Synthetic data is generated data that is used to mimic real data. There are many reasons why one might want to generate synthetic data. For example, synthetic data can be used to test data-driven models when real data is not available. Synthetic data can also be used to protect the privacy of individuals in data sets.3.1. Basic Functions Used In Generating Synthetic Data3.1.1. Creating a Value Vector from a Known Univariate Distribution3.1.2. Vector Generation from a Multi-levels Categorical Variable3.1.3. Multivariate3.1.4. Multivariate (with correlation) 3.2. Multivariate Imputation Via Mice Package in R3.2.1. Example of MICE3.3. Augmented Data3.4. Image Augmentation Using Torch Package3.5. Generating Synthetic Data with The "conjurer" Package in R3.5.1. Create a Customer3.5.2. Create a Product3.5.3. Creating Transactions3.5.4. Generating Synthetic Data3.6. Generating Synthetic Data With “Synthpop” Package In R3.7. Copula3.7.1. t Copula3.7.2. Normal Copula3.7.3. Gaussian CopulaChapter 4: GANs.... 15 pagesChapter Goal: This book chapter aims to provide information on the content and purpose of GANs. GANs are a type of artificial intelligence that is used to generate new data that is similar to the training data. This is done by training a generator network to produce data that is similar to the training data. The generator network is trained by using a discriminator network, which is used to distinguish between the generated data and the training data. 4.1. GANs4.2. CTGAN4.3. SurfelGAN4.4. Cycle GANs4.5. SinGAN4.6. DCGAN4.7. medGAN4.8. WGAN4.9. seqGAN4.10. Conditional GANChapter 5: Synthetic Data Generation with Python.... 40 pagesChapter Goal: The purpose of this chapter is to provide information about the methods of synthetic data generation with Python. Python is a widely used high-level programming language that is known for its ease of use and readability. It has a large standard library that covers a wide range of programming tasks.5.1. Data Generation with Know Distribution5.2. Synthetic Data Generation in Regression Problem5.3. Gaussian Noise Apply to Regression Model5.4. Friedman Functions and Symbolic Regression5.5. Synthetic data generation for Classification and Clustering Problems5.6. Clustering Problems5.7. Generation Tabular Synthetic Data by Applying GANs

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Book SynopsisUnderstand real-world game development concepts using JavaFX game engine called FXGL. The core focus of the book is on developing a standalone game or application with FXGL. We will start with an overview of the book followed by requisite concepts from Java and JavaFX that will be used throughout this book. Next, we will learn about the FXGL game engine and its wide range of real-world game development techniques. In the following chapter, we learn about entity-component model used in FXGL to create a powerful abstraction of the game world. The next chapter builds on this, where we develop a platformer game using the physics engine and a popular external tool called Tiled. An important concept of games AI is covered in the following chapter. Visually complex features related to graphics and rendering as well as UI elements and animation system in FXGL will be discussed in the next chapter. The following chapter is dedicated to non-game applications that can be developed usTable of ContentsChapter 1: IntroductionChapter Goal: Sets the scene for the book, provides an overview and sets expectationsChapter 2: Requisite Java and JavaFX ConceptsChapter Goal: Covers fundamental knowledge required to understand the book contentSub-topics: Java programmingJavaFX scene graphJavaFX model of programmingJavaFX conceptsChapter 3: FXGL ArchitectureChapter Goal: Provides an overview of the FXGL architecture, features, and capabilitiesChapter 4: Entity-Component Case Study: Develop Arcade GamesChapter Goal: Introduction to entity-component model used for abstracting game worldsSub-topics: Game worldEntity-Component modelPong and Breakout style gamesChapter 5: Physics Case Study: Develop a Platformer Game Chapter Goal: Introduction to lightweight and heavyweight physics engines in FXGLSub-topics: Collision detectionRigid body dynamicsMario style gameChapter 6: AI Case Study: Develop a Maze Action GameChapter Goal: Provides a foundation for using and developing AI agents in FXGLSub-topics: A* pathfindingGraph theoryComponent-driven behaviorPac-man style gameChapter 7: Graphics and UI Case Study: Develop a Top-Down Shooter GameChapter Goal: Introduction to the particle and animation systems used in FXGLSub-topics: Particle systemMulti-layer renderingAnimationsInterpolationsGeometry wars style gameChapter 8: Developing General-Purpose ApplicationsChapter Goal: Provide information on how FXGL can be used in non-game contextsChapter 9: Cross-platform DeploymentChapter Goal: Demonstrates the package and deployment process with FXGLSub-topics: jlinkNative imagesGluon toolsMobile developmentChapter 10: ConclusionChapter Goal: Recap what was covered in the chapters, provides external resources and ideas for future projects

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Book SynopsisReview the fundamental constructs in C# using Q&As and program segments to boost your confidence and gain expertise. This book will help you analyze your programs more efficiently and enhance your programming skills.The book is divided into three parts, where you will learn the fundamentals, object-oriented programming, and some advanced features of C#. In the first part, you will review C# and .NET basics along with the important constructs such as strings, arrays, and structures. In the second part, you''ll review the concepts of object-oriented programming in detail. Here, you will go through various program segments in class and objects, inheritance, polymorphism, abstraction, encapsulation, and much more. You will also analyze the output of the given programs with the help of Q&A sections. The uses of interfaces, static class, and exception handling are discussed in the book along with some other important concepts in C#. In the third and last part, you will learn aTable of ContentsPart I Fundamentals Chapter 1: Language Basics Chapter Goal: This chapter discusses the following topics: The important concepts in .NET The basic programming constructs in C#. Use of some useful data types including the var type. Use of some useful operators and explicit-casting. Use of the selection statements and case guards. Use of iteration statements. Use of the jump statements. Use of the ternary operator. No of pages: 28 Sub - Topics N Chapter 2: String and Arrays Chapter Goal: Once you finish this chapter, you can answer the following questions and related areas: How can you use string datatype in your program? How can you use the common in-built methods from the String class? How a String variable is different from a StringBuilder? How can you convert a string to an int? How can you use nullable reference type in a program? How to create arrays in C#? What are the different types of C# arrays and how to use them? How to use common in-built methods from the System.Array class? How can you iterate over a string or an array? No of pages 23 Sub - Topics NA Chapter 3: Enumeration and Structures Chapter Goal: This chapter discusses the following topics: The enum fundamentals Flags enumeration Defaut value expressions The struct fundamentals Non-destructive mutations No of pages: 26 Sub - Topics NA Part II Object-Oriented Programming Chapter 4: Class and Objects Chapter Goal: This chapter focuses on the following topics: Classes and objects creations. Instance fields and methods. Constructors and their usage. Optional parameters. Object initializers. Nested classes. The uses of private, internal, and public modifiers inside a class. No of pages: 17 Sub - Topics NA Chapter 5: Inheritance Chapter Goal: This chapter covers the following topics: Inheritance and types. Method and constructor overloading. Method overriding. Use of virtual, override, and new keywords. Use of the sealed keyword. Introductory discussion on covariance and contravariance No of pages 33 Sub - Topics NA Chapter 6: Polymorphism Chapter Goal: This chapter helps you to review: Polymorphism and its benefits. Abstract classes and their uses. Interfaces and their uses. Different types of interfaces. Writing polymorphic codes using abstract classes and interfaces No of pages: 30 Sub - Topics NA Chapter 7: Encapsulation Chapter Goal: This chapter covers the following topics: What is encapsulation? How is it different from an abstraction? Properties and their usage. Different ways to create a property. The usage of the get and set accessors. Virtual and abstract properties. The discussion of the init accessor. Indexers and their usage. How can the indexers and properties work with an interface? Discussion on different aspects of properties and indexers. No of pages: 26 Sub - Topics NA Chapter 8: Exception Handling Chapter Goal: This chapter covers the following topics: Exception and its uses in C# programming. Use of the try, catch, and finally blocks. Use of multiple catch blocks in a program. Use of a general catch block. How to throw and re-throw an exception. Use of exception filters. Custom exception class and its usage. No of pages: 23 Sub - Topics NA Chapter 9: Useful Concepts Chapter Goal: Q&A and program segments on some useful constructs such as casting and boxing, static class and methods, passing value type by value, passing value type by references(using ref and out keyword), extension methods, and so on. No of pages: 15-25+ Sub - Topics NA Part III Advanced Features Chapter 10: Delegates Chapter Goal: This chapter covers the following topics: Delegates and their uses Multicast delegates Some commonly used in-built delegates Covariance and contravariance using delegates No of pages: 20 Sub - Topics NA Chapter 11: Events Chapter Goal: This chapter helps you to review your understanding of events and discusses the following: Events creation and their uses. How to pass the event data. Use of event accessors. Use of interface events (both implicit and explicit). Simplified coding with events. No of pages: 22 Sub - Topics NA Chapter 12: Lambdas Chapter Goal: This chapter focuses on this and covers the following topics: Lambda expressions and their use Expression-bodied members Use of local variables inside lambda expressions. Event handling using lambda expressions. Use of a static lambda. Understanding natural type. No of pages: 22 Sub - Topics NA Chapter 13: Generics Chapter Goal: This chapter focuses on the following topics: The motivation behind generics. The fundamentals of generic programs. Use of generic interfaces. Use of generic constraints. Use of covariance and contravariance using generics. Self-referencing generic type. Experimenting with generic method’s overloading and overriding. Analyzing the static data in the context of generics. No of pages: 35 Sub - Topics NA Chapter 14: Multithreading Chapter Goal: Upon completion of this chapter, you’ll be able to answer the following questions: What are the threads and how can you create them? What is a multithreaded program? How does it differ from a single-threaded application? Why are the ThreadStart and ParameterizedThreadStart delegates important in thread programming? How to block a thread using Sleep or Join methods? How can you use lambda expressions in a multithreaded program? How to use important Thread class members? How a foreground thread is different from a background thread? What is synchronization and why is it needed? How can you implement thread safety in C# using lock statements? How can you implement an alternative approach to lock statements using Monitor’s Entry and Exit Method? What is a deadlock and how can you detect the deadlock in your system? What is the purpose of using the ThreadPool class? What are the associative pros and cons of using it? How to cancel a running thread in the managed environment? And many more. No of pages: 40 Sub - Topics NA Chapter 15: Miscellaneous Chapter Goal: Q&A and program segments on the related latest features that are covered in the previous chapters and any other important topics(if any). No of pages: 10-20+ Sub - Topics NA

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Book SynopsisLearn how to program in modern C, from the basics through the advanced topics required for proficiency. This book is the fastest path to C fluency for anyone experienced in a general-purpose programming language. From start to finish, code examples highlight the idioms and best practices behind efficient, robust programs in a variety of areas.The book opens with a thorough coverage of syntax, built-in data types and operations, and program structure. C has quirks and presents challenges, which are covered in detail. The coverage of advanced features is what sets this book apart from others. Among the advanced topics covered are floating-point representation in the IEEE 754 standard; embedded assembly language in C code for overflow detection; regular expressions, assertions, and internationalization; WebAssembly through C; and software libraries for C and other clients. Memory  efficiency and safety are the two major challenges in C programming, andTable of Contents1. Program Structure2. Basic Data Types3. Aggregates and Pointers4. Storage Classes5. Input and Output6. Networking7. Concurrency and Parallelism8. Miscellaneous Topics

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Book Synopsis This book introduces the Kotlin programming skills and techniques necessary for building applications. You''ll learn how to migrate your Java programming skills to Kotlin, a Java Virtual Machine (JVM) programming language. The book starts with a quick tour of the Kotlin language and gradually walks you through the language in greater detail over the course of succeeding chapters. You''ll learn Kotlin fundamentals like generics, functional programming, type system, debugging, and unit testing. Additionally, with the book''s freely downloadable online appendices, you''ll discover how to use Kotlin for building Spring Boot applications, data persistence, and microservices.  What You Will Learn Learn the Kotlin language, its functions, types, collections, generics, classes, and more Dive into higher-order functions, generics, debugging, and unit testing Apply the fundamentals of KotlinTable of ContentsPart 1: Kotlin1. Setup2. Tour of the Kotlin language3. Functions4. Types5. Higher order functions6. Collections7. Generics8. Classes9. Unit Testing10. Java InteroperabilityPart 2: Spring Boot11. Spring and SpringBoot12. Setup13. Getting started with a projecta. Using the project initializrb. Auto restarting an appc. Views and backing beansd. Views and controller functionse. Servicesf. Posting to a controllerg. Dependency Injection14. Functional Programminga. Overviewb. Function parametersc. Listsd. Filter and flatMape. Reduce and Foldf. Maps15. Hibernatea. Adding the dependenciesb. Entitiesc. Persisting to a database16. Reflectiona. Overviewb. Ins

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Book SynopsisWritten as an illustrated, step-by-step guide, this book will introduce you to Python with examples using the latest version of the language.  You''ll begin by learning to set up your Python environment. The next few chapters cover the basics of Python such as language classifications, Python language syntax, and how to write a program. Next, you will learn how to work with variables, basic data types, arithmetic, companion, and Boolean operators, followed by lab exercises. Further, the book covers flow control, using functions, and exception handling, as well as the principles of object-oriented programming and building an interface design. The last section explains how to develop a game by installing PyGame and how to use basic animation, and concludes with coverage of Python web development with web servers and Python web frameworks.  The Absolute Beginners Guide to Python Programming will give you the tools, confidence, and insTable of ContentsChapter 1: What is Python.Goal: About Python, what it is, how to set up the interpreter on machineSub-topicsSetting UpChapter 2: The BasicsGoal: Covers basics, syntax, writing a basic program and executing the codeSub-topicsLanguage ClassificationLow-Level LanguageHigh-Level LanguagePython Language SyntaxReserved WordsIdentifiersIndentationCommentsInputOutputEscape CharactersWriting a ProgramChapter 3: Working with DataGoal: Covers data types: integers, lists, strings, etc, etc , variables, operatorsSub-topicsVariablesLocal VariablesGlobal VariablesBasic Data TypesIntegersFloating Point NumbersStringsListsTwo Dimensional ListsSetsTuplesDictionariesCasting Data TypesArithmetic OperatorsOperator PrecedencePerforming ArithmeticComparison OperatorsBoolean OperatorsBitwise OperatorsLab ExercisesChapter 4: Flow ControlGoal: Explains flow control, sequence, if/elif, for/whileSub-topicsSequenceSelectionif...elseelifIteration (Loops)For loopWhile loopBreak and ContinueLab ExercisesChapter 5: Handling FilesGoal: Explains file handling, reading files, writing to files, text files, binary filesFile TypesText FileBinaryText File OperationsOpen FilesWrite to a FileRead from a FileBinary File OperationsOpen FilesWrite to a FileRead a FileRandom File AccessLab ExercisesChapter 6: Using FunctionsSub-topicsDeclaring FunctionsRecursionLab ExercisesChapter 7: Exception HandlingGoal: Covers exception and error handlingSub-topicsTypes of ExceptionCatching ExceptionsRaising your Own ExceptionsChapter 8: Object Oriented ProgrammingGoal: OOP principles, classes, objects and inheritanceSub-topicsPrinciples of OOPEncapsulationInheritancePolymorphismAbstractionClasses & ObjectsClass InheritancePolymorphic ClassesMethod OverridingChapter 9: Building an InterfaceGoal: Building an interface using tkinterSub-topicsCreating a WindowAdding WidgetsMenusThe CanvasImagesButtonsMessage BoxesText FieldListboxCheckboxLabelsLabel FrameInterface DesignChapter 10: Developing a GameSub-topicsInstalling PyGameOpening a WindowAdding an ImageThe Game LoopThe Event LoopShapesBasic AnimationChapter 11: Python Web DevelopmentSub-topicsWeb ServersExecuting a ScriptPython Web FrameworksQuick ReferenceData TypesNumeric OperatorsComparison OperatorsBoolean OperatorsString OperatorsList OperatorsDictionary OperatorsString MethodsList MethodsDictionary MethodsFunctionsFilesConditionalMulti ConditionalWhile LoopFor LoopLoop ControlModulesBuilt in FunctionsDeclare a ClassChild ClassCreate ObjectCall Object Method

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Book SynopsisIntermediate to AdvancedTable of Contents1. System.- 2. Application.- 3. Activities.- 4. Services.- 5. Broadcasts 6. Content Providers.- 7. Permissions.- 8. APIs.- 9. User Interface.- 10. Development.- 11. Building.- 12. Communication.- 13. Hardware.- 14. Testing.- 15. Troubleshooting.- 16. Distributing Apps.- 17. Instant Apps.- 18. CLI.

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Book SynopsisUse the popular Spring Data project for data access and persistence using various Java-based APIs such as JDBC, JPA, MongoDB, and more.  This book shows how to easily incorporate data persistence and accessibility into your microservices, cloud-native applications, and monolithic enterprise applications. It also teaches you how to perform unit and performance testing of a component that accesses a database. And it walks you through an example of each type of SQL and NoSQL database covered.   After reading this book, you''ll be able to create an application that interacts with one or multiple types of databases, and conduct unit and performance testing to analyze possible problems. Source code is available on GitHub. What You''ll Learn Become familiar with the Spring Data project and its modules for data access and persistence Explore various SQL and NoSQL persistence types Uncover the persistence and domain models, and handle transaction management for SQL Migrate database changes and versioning for SQL Dive into NoSQL persistence with Redis, MongoDB, Neo4j, and Cassandra Handle reactive database programming and access with R2DBC and MongoDB Conduct unit, integration, and performance testing, and more Who This Book Is For Experienced Java software application developers; programmers with experience using the Spring framework or the Spring Boot micro framework Table of ContentsPart I - IntroductionChapter 1: Architecture of the ApplicationsChapter 2: Spring Basics and BeyondChapter 3: Spring Data and Persistence TypesPart II - SQL PersistenceChapter 4: Persistence and Domain ModelChapter 5: Transaction ManagementChapter 6: Versioning or Migrate the Changes of the DatabasePart III - NO-SQL PersistenceChapter 7: Redis key/value DatabaseChapter 8: MongoDB Document DatabaseChapter 9: Neo4j Graph DatabaseChapter 10: Cassandra wide-column DatabaseChapter 11: Reactive access w/R2DBC and MongoDBChapter 12: Unit/Integration TestingChapter 13: Performance TestingChapter 14: Best PracticesPart I - IntroductionThis part or section contains all the introduction about the basics of the Spring and the architecture of theapplication to use the persistence.Chapter 1: Architecture of the applicationsChapter Goal: In this chapter, the readers will see the different ways of structuring one application and thebest practices to organize all the things related to persistence like the use of DAO (repositories on Spring).• Small history of the methods of persistence (Plain query using the class of Java, ORM)• Different types of architectureso Layerso Hexagonal or onion• Persistence design patterso DAO (Repositories in Spring)o Data Transfer Object (DTO)Chapter 2: Spring basics and beyondChapter Goal: In this chapter, the readers will see the different ways of structuring one application and thebest practices to organize all the things related to persistence like the use of DAO (repositories on Spring).• Spring’s Architecture • Dependency Injection and Inversion of Control• Basic Application SetupChapter 3: Spring Data and different types of persistenceChapter Goal: This chapter will provide a full explanation about Spring Data, how it works and what this librarydoes behind the scenes.• How the Spring Data works• How the Repositories workso Using interfaceso Defining a custom implementationPart II - SQL persistenceThis part or section contains the information about different aspects of the persistence of databases whichhave a rigid schema. Also, the readers will see different strategies of deploying the changes on the schemas.Chapter 4: Persistence and domain modelChapter Goal: In this chapter, the readers will learn the basics about persistence and how it works behind thescenes. Also, the readers will see how to create validations in the schema like the size of the column and thedifferent types of relationship between entities.• JPA configuration using annotationso Entity, Ido Types of relationshipso Pre-update, pre-persist• Ways to define the querieso Using specificationso Define SQL• How validate the schema• Types of InherenceChapter 5: Transaction managementChapter Goal: In this chapter, the readers will learn the basics of the transactions and some concepts of ACID.• Definition of ACID• Isolation Levels• Transactional levelsChapter 6: Versioning or migrate the changes of the databaseChapter Goal: In this chapter the readers will see different tools or strategies to include the changes of thedatabases, e.g use Liquibase/Flyway, running the scripts manually, or using the auto-update of the Spring.Also, this chapter will include some mechanism to move the data from one column to another using featureflags.• Mechanism to migrate the changes• Tools to versioning the changeso Liquibaseo Flyway• Using Feature Flags to new featureso What is a Feature flag?o Benefits of use this approacho Common librariesPart III - NO-SQL persistenceIn this section the idea is to cover one example of each type of the databases NO-SQL like key/value,document, graph, and wide-column database. The idea is not to cover all more than one example of a type ofdatabase because most of them have certain operations similar.Chapter 7: Redis key/value databaseChapter Goal: In this chapter, the readers will see how to run a database and save the information using aspecific key. Also, this chapter will show the readers to create a serializer to persist data that is complex andsome best practices like persist the information in async mode.The last point is how to configure the TTL in the information that the readers persist in the database.• What is Redis and which are the benefits?• Connecting with multiples Redis• Persist synchronous or asynchronous• Object Mapping and ConversionChapter 8: MongoDB Document databaseChapter Goal: In this chapter, the readers will see how to run a mongo database and how to persist theinformation with the definition of the entities using the different operations that are permitted on MongoDB.• What is a document store?• Setting up a Mongo• Access using repositories• Manage transactions in a programmatic wayChapter 9: Neo4j Graph databaseChapter Goal: In this chapter, the readers will see how to run a database and how to create different types ofqueries. Also the reader will see the different aspects of the persistence of the information and the use ofreactive approach.• Modeling the problem as a Grapho Cases of usero Benefits• Persisting the domain• Manage transactionsChapter 10: Cassandra wide-column databaseChapter Goal: In this chapter, the readers will see how to configure the database on Spring and thedeclaration of the entities that need to be used to persist the information. Also, the different ways topersist or modify the information on Cassandra.The last point is how to configure the TTL in the information that the readers persist in the database.• What is Cassandra and how works?• Configuration for Cassandra• Access using repositories• Defining a TTLPart IV – Advanced, testing and best practicesThis part covers some aspects of any type of database to create different types of tests and validate theperformance of the queries. Also, this section covers some best practices to reduce the possible problems ormistakes in the applications that access a database.Chapter 11: Reactive accessChapter Goal: This chapter needs to cover how you can access and obtain the information in a reactive way.• What is reactive access?• Modifying queries to be reactiveo R2DBCo MongoDBChapter 12: Unit/Integration testingChapter Goal: This chapter needs to cover more in detail how you can write unit tests without using anexistent database but using the same motor of the database, to do this the reader will use Test Cointainerswith Junit 5 which is the standard to write unit tests.• Unit Testing with Mocks• Integration Testing with a Databaseo What is Test containers?o Test Containers vs embeddedo How you can use it?o Possible problems in the pipelineChapter 13: Performance testingChapter Goal: In this chapter the reader will use some tools like Gatling or QuickPerform to see how tocreate a performance test and analyze if the queries have some issue related with the use ofCPU/memory.• How check or analyze the performance of the queries?• Analyzing the complexity of queries• Performance test of an endpoint that access to a DBChapter 14: Best practicesChapter Goal: In this chapter the reader will know some strategies to improve the performance of thedatabase including some mechanism of cache to reduce the number of times that anyone accesses toobtain information. • Access to the information◦ Master-slave • Using cache to reduce the accessed to DB • Compress the information • Lazy Loading Issues • Pagination and ways to reduce the response

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Book SynopsisLearn the Spring Boot 3 micro framework and build your first Java-based cloud-native applications and microservices. Spring Boot is the lightweight, nimbler cousin to the bigger Spring Framework, with plenty of bells and whistles. This updated edition includes coverage of Spring Native, which will help you speed up your Spring Boot applications, as well as messaging with Spring Boot, Spring GraphQL, Spring Data JDBC and reactive relational database connectivity (R2DBC) with SQL.This new edition also covers enhancements to actuator endpoints, MongoDB 4.0 support, layered JAR and WAR support, support to build OCI images using Cloud Native Build Packs, changes to the DataSource initialization mechanism, and how bean validation support has moved to a separate spring-boot-validation-starter module. This book will teach you how to work with relational and NoSQL databases for data accessibility using Spring Boot with Spring Data, how to persist data with the Java PersTable of ContentsChapter - 1: Introduction to Spring Boot Chapter - 2: Getting Started with Spring Boot Chapter - 3: Spring Boot Essentials Chapter - 4: Web Applications with Spring Boot Chapter - 5: Working with JDBC Chapter - 6: Working with MyBatis Chapter - 7: Working with JOOQ Chapter - 8: Working with JPA Chapter - 9: Working with MongoDB Chapter - 10: Building REST APIs Using Spring Boot Chapter - 11: Reactive Programming Using Spring WebFlux Chapter - 12: Securing Web Applications Chapter - 13: Spring Boot Actuator Chapter - 14: Testing Spring Boot Applications Chapter - 15: GraphQL with Spring Boot Chapter - 16: Deploying Spring Boot Applications Chapter - 17: Spring Boot Autoconfiguration Chapter - 18: Creating a Custom Spring Boot Starter Chapter - 19: Spring Boot With Kotlin, Scala, and Groovy Chapter - 20: Introducing JHipster Chapter - 21: Spring Native

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Book SynopsisChapter 1:  Introduction to AI & the AI Ecosystem.- Chapter 2:  AI Best Practise & DataOps.- Chapter 3: Data Ingestion for AI.- Chapter 4: Machine Learning on Cloud.- Chapter 5: Neural Networks and Deep Learning.- Chapter 6: The Employer's Dream: AutoML, AutoAI and the rise of NoLo UIs.- Chapter 7: AI Full Stack: Application Development.- Chapter 8: AI Case Studies.- Chapter 9: Deploying an AI Solution (Productionizing & Containerization).- Chapter 10: Natural Language Processing.- Postscript.Table of ContentsChapter 1: Introduction to AI & the AI EcosystemChapter Goal: Embracing the hype and the pitfalls, introduces the reader to current and emerging trends in AI and how many businesses and organisations are struggling to get machine and deep learning operationalizedNo of pages: 30Sub -Topics1. The AI ecosystem2. Applications of AI3. AI pipelines4. Machine learning5. Neural networks & deep learning6. Productionizing AIChapter 2: AI Best Practise & DataOpsChapter Goal: Help the reader understand the wider context for AI, key stakeholders, the importance of collaboration, adaptability and re-use as well as DataOps best practice in delivering high-performance solutionsNo of pages: 20Sub - Topics 1. Introduction to DataOps and MLOps 2. Agile development3. Collaboration and adaptability4. Code repositories5. Module 4: Data pipeline orchestration6. CI / CD7. Testing, performance evaluation & monitoringChapter 3: Data Ingestion for AIChapter Goal: Inform on best practice and the right (cloud) data architectures and orchestration requirements to ensure the successful delivery of an AI project.No of pages : 20Sub - Topics: 1. Introduction to data ingestion2. Data stores for AI3. Data lakes, warehousing & streaming4. Data pipeline orchestrationChapter 4: Machine Learning on CloudChapter Goal: Top-down ML model building from design thinking, through high level process, data wrangling, unsupervised clustering techniques, supervised classification, regression and time series approaches before interpreting results and algorithmic performance No of pages: 20Sub - Topics: 1. ML fundamentals2. EDA & data wrangling3. Supervised & unsupervised machine learning4. Python Implementation5. Unsupervised clustering, pattern & anomaly detection6. Supervised classification & regression case studies: churn & retention modelling, risk engines, social media sentiment analysis7. Time series forecasting and comparison with fbprophetChapter 5: Neural Networks and Deep LearningChapter Goal: Help the reader establish the right artificial neural network architecture, data orchestration and infrastructure for deep learning with TensorFlow, Keras and PyTorch on CloudNo of pages: 40Sub - Topics: 1. An introduction to deep learning2. Stochastic processes for deep learning3. Artificial neural networks4. Deep learning tools & frameworks5. Implementing a deep learning model6. Tuning a deep learning model7. Advanced topics in deep learningChapter 6: The Employer’s Dream: AutoML, AutoAI and the rise of NoLo UIsChapter Goal: Building on acquired ML and DL skills, learn to leverage the growing ecosystem of AutoML, AutoAI and No/Low code user interfacesNo of pages: 20Sub - Topics: 1. AutoML2. Optimizing the AI pipeline3. Python-based libraries for automation4. Case Studies in Insurance, HR, FinTech & Trading, Cybersecurity and Healthcare5. Tools for AutoAI: IBM Cloud Pak for Data, Azure Machine Learning, Google Teachable MachinesChapter 7: AI Full Stack: Application Development Chapter Goal: Starting from key business/organizational needs for AI, identify the correct solution and technologies to develop and deliver “Full Stack AI”No of pages: 20Sub - Topics: 6. Introduction to AI application development7. Software for AI development8. Key Business applications of AI:• ML Apps• NLP Apps• DL Apps4. Designing & building an AI applicationChapter 8: AI Case StudiesChapter Goal: A comprehensive (multi-sector, multi-functional) look at the main AI use uses in 2022No of pages: 20Sub - Topics: 1. Industry case studies2. Telco solutions3. Retail solutions4. Banking & financial services / fintech solutions5. Oil & gas / energy & utilities solutions6. Supply chain solutions7. HR solutions8. Healthcare solutions9. Other case studiesChapter 9: Deploying an AI Solution (Productionizing & Containerization)Chapter Goal: A practical look at “joining the dots” with full-stack deployment of Enterprise AI on CloudNo of pages: 20Sub - Topics: 1. Productionizing an AI application2. AutoML / AutoML3. Storage & Compute4. Containerization5. The final frontier…

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Book SynopsisLearn how to automate processes, visualize your data, and improve productivity using Power Apps, Power Automate, Power BI, SharePoint, Forms, Teams, and more. This book will help you build complete solutions that often involve storing data in SharePoint, creating a front-end application in Power Apps or Forms, adding additional functionality with Power Automate, and effective reports and dashboards in Power BI.This new edition greatly expands the focus on Power Apps, Power BI, Power Automate, and Teams, along with SharePoint and Microsoft Forms. It starts with the basics of programming and shows how to build a simple email application in .NET, HTML/JavaScript, Power Apps on its own, and Power Apps and Power Automate in combination. It then covers how to connect Power Apps to SharePoint, create an approval process in Power Automate, visualize surveys in Power BI, and create your own survey solution with the combination of a number of Microsoft 365 tools. You''ll work with an eTable of ContentsChapter 1. Programming in the Power Platform In this chapter, we will cover the basics of programming: properties, methods, and events. We will then look at how their implementation differs between in each of the Power Platform applications compared with traditional environments like .NET (Windows Forms and ASP.NET) and JavaScript. For Power Apps, we will see how you can set the properties of other objects directly but instead need to make the value of what you want to change (such as the text of a button) a variable and then change the value of that variable elsewhere in the application. We will also explore its Visual Basic-like syntax. In Power Automate, we will look at the different types of actions (variables, loop, parallel branches, conditions, etc.). Finally, in Power BI we will look at the Power Query M formula language. Chapter 2. Updating a SharePoint List Using Power Apps Chapter 3. Creating an Approval Process with Power Automate Chapter 4. Creating a Survey Response Dashboard with Microsoft Power BI Chapter 5. Creating a Survey Solution with Microsoft Forms, Flow, SharePoint, and Power BI Chapter 6. Power BI Challenges with JSON, XML, and Yes/No Data Chapter 7. Power BI Case Study: Monitoring BMC Remedy Help Tickets Chapter 8. Building a Help Ticketing System in PowerApps and SharePoint – New Ticket Form Chapter 9. Continuing the Help Ticketing System – Technician Form Chapter 10. Using Power BI for the Help Ticketing System Chapter 11. Overcoming Power Apps Delegation Issues with SharePoint Data Sources In this chapter, we look at how to use the technique described in https://tinyurl.com/twzvbgl to overcome delegation limits in Power Apps using a SharePoint data source. We also implement a corresponding Power Automate Flow to copy the ID value to a numeric column each time we create a record. Chapter 12. Creating a Class Sign-Up Solution in SharePoint and Power Apps Chapter 13. Visualizing Learning Management Data from SQL Server using Power BI This chapter gives several examples of connecting to multiple tables in a SQL Server database in order to visualize test score, completion, assignments and similar learning management data. It demonstrates custom columns, merging tables, slicers, and much more. Chapter 14. Dynamic Information in Power Apps and Sending an Adaptive Card to Teams using Power Automate In this chapter, we create linked SharePoint lists that display status levels, colors, and associated steps. We then read these lists from PowerApps to create a status display that we display in a tab in Teams. Finally, we create a Power Automate flow so that each time the status changes, we automatically post that to our Teams channel. Chapter 15. Dynamically Setting Object Properties in Power Apps Based on a SharePoint List In this chapter, we explore how to approximate dynamic object references/reflection in Power Apps. We take an "Actions" list in SharePoint and use it to set Text, Tooltip, and Visible properties of each corresponding button in Power Apps. Chapter 16. Uploading Files from PowerApps to SharePoint and Emailing Links using Power Automate In this chapter, we see how to upload multiple attachments from Power Apps to a SharePoint document library using Power Automate. While we are in Power Automate, we create an email of links to these documents and email it to the designated recipient. Chapter 17. Working with SharePoint Lookup Columns in Power BI In this chapter, we explore how to use the FieldValuesAsText functionality in Power BI to get the data from within a SharePoint Lookup column. We also explore creating custom columns and filtering by Content Type. Chapter 18. Joining SharePoint/Excel Tables in Power BI This chapter shows two different examples on how to join data in Power BI to make effective visualizations. The first example shows how to access lookup columns in SharePoint lists by doing a join on the lists once you bring them into Power BI. The second one is an extended example on how to verify data between two Excel spreadsheets that share a common value. We first show how to accomplish the task with Microsoft Access with a join query and a set of custom columns that reflect whether columns between the two spreadsheets actually match. We then show how it is easier and more reproducible with later data to do the same thing with Power BI using a merge query. Chapter 19. Copying Microsoft Forms Answers to SharePoint using Power Automate and then Showing the Most Current Submission in Power BI In this chapter, we take a simple Microsoft Form, copy each entry to SharePoint with Power Automate, and then visualize the data in Power BI. The main insight on the Power BI side is to show only the most recent form submission by grouping within Power BI, creating a MaxDate column, and then filtering. Chapter 20. Copying Microsoft Forms Attachments to a SharePoint List Item using Power Automate In this chapter, we see how to create a group form in Microsoft Forms, create an associated SharePoint List to hold the data, use Power Automate to copy the form responses to the list, and, most importantly, copy each file uploaded with the form and attach it to the corresponding list item. Chapter 21. Creating an Employee Recognition App in Power Apps, Power Automate, Power BI, Teams, and SharePoint In this chapter, we demonstrate how to create a Power Apps and Power Automate employee recognition solution that can post the recognition to a Teams channel, send a Teams chat, and/or send via email. We try to make it optional for submitters to include their information, finding it works for chats and email but not posts. We store the information in SharePoint and then use Power BI to visualize the values demonstrated and other data. Chapter 22. Creating a Reservations Booking Solution in Power Apps and SharePoint In this chapter, we demonstrate how to create a SharePoint list of available appointments and then use Power Apps to allow users to select an available appointment and make that not available to anyone else. It also shows how to allow users to edit or delete their appointments (or those created on their behalf). Chapter 23. Creating a Scoring Application in Power Apps and SharePoint In this chapter, we create a scoring application where we patch three different SharePoint records at the same time. Along the way, we use cascading drop-down lists, collections, data tables, and variables.

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Book SynopsisIntermediate-Advanced user levelTable of Contents1: Introduction to Numerical Methods in Kotlin.- 2: Linear Algebra.- 3: Finding Roots of Equations.-4: Finding Roots of Systems of Equations.- 5: Curve Fitting and Interpolation.- 6: Numerical Differentiation and Integration.- 7: Ordinary Differential Equations.- 8: Partial Differential Equations.- 9: Unconstrained Optimization.- 10: Constrained Optimization.- 11: Heuristics.- 12: Basic Statistics.- 13: Random Numbers and Simulation.- 14: Linear Regression.- 15: Time Series Analysis.- References.Table of ContentsAbout the Authors...........................................................................................................iPreface............................................................................................................................ii1. Why Kotlin?..............................................................................................................61.1. Kotlin in 2022.....................................................................................................61.2. Kotlin vs. C++....................................................................................................61.3. Kotlin vs. Python................................................................................................61.4. Kotlin in the future .............................................................................................62. Data Structures.......................................................................................................72.1. Function...........................................................................................................72.2. Polynomial ......................................................................................................73. Linear Algebra .......................................................................................................83.1. Vector and Matrix ...........................................................................................83.1.1. Vector Properties .....................................................................................83.1.2. Element-wise Operations.........................................................................83.1.3. Norm ........................................................................................................93.1.4. Inner product and angle ...........................................................................93.2. Matrix............................................................................................................103.3. Determinant, Transpose and Inverse.............................................................103.4. Diagonal Matrices and Diagonal of a Matrix................................................103.5. Eigenvalues and Eigenvectors.......................................................................103.5.1. Householder Tridiagonalization and QR Factorization Methods..........103.5.2. Transformation to Hessenberg Form (Nonsymmetric Matrices)...........104. Finding Roots of Single Variable Equations .......................................................114.1. Bracketing Methods ......................................................................................114.1.1. Bisection Method ...................................................................................114.2. Open Methods...............................................................................................114.2.1. Fixed-Point Method ...............................................................................114.2.2. Newton’s Method (Newton-Raphson Method) .....................................114.2.3. Secant Method .......................................................................................114.2.4. Brent’s Method ......................................................................................115. Finding Roots of Systems of Equations...............................................................125.1. Linear Systems of Equations.........................................................................125.2. Gauss Elimination Method............................................................................125.3. LU Factorization Methods ............................................................................125.3.1. Cholesky Factorization ..........................................................................125.4. Iterative Solution of Linear Systems.............................................................125.5. System of Nonlinear Equations.....................................................................126. Curve Fitting and Interpolation............................................................................146.1. Least-Squares Regression .............................................................................146.2. Linear Regression..........................................................................................146.3. Polynomial Regression..................................................................................146.4. Polynomial Interpolation...............................................................................146.5. Spline Interpolation .......................................................................................147. Numerical Differentiation and Integration...........................................................157.1. Numerical Differentiation .............................................................................157.2. Finite-Difference Formulas...........................................................................157.3. Newton-Cotes Formulas................................................................................157.3.1. Rectangular Rule....................................................................................157.3.2. Trapezoidal Rule....................................................................................157.3.3. Simpson’s Rules.....................................................................................157.3.4. Higher-Order Newton-Coles Formulas..................................................157.4. Romberg Integration .....................................................................................157.4.1. Gaussian Quadrature..............................................................................157.4.2. Improper Integrals..................................................................................158. Numerical Solution of Initial-Value Problems....................................................168.1. One-Step Methods.........................................................................................168.2. Euler’s Method..............................................................................................168.3. Runge-Kutta Methods...................................................................................168.4. Systems of Ordinary Differential Equations.................................................169. Numerical Solution of Partial Differential Equations..........................................179.1. Elliptic Partial Differential Equations...........................................................179.1.1. Dirichlet Problem...................................................................................179.2. Parabolic Partial Differential Equations........................................................179.2.1. Finite-Difference Method ......................................................................179.2.2. Crank-Nicolson Method.........................................................................179.3. Hyperbolic Partial Differential Equations.....................................................1710..................................................................................................................................1811..................................................................................................................................1912. Random Numbers and Simulation ....................................................................2012.1. Uniform Distribution .................................................................................2012.2. Normal Distribution...................................................................................2012.3. Exponential Distribution............................................................................2012.4. Poisson Distribution ..................................................................................2012.5. Beta Distribution........................................................................................2012.6. Gamma Distribution ..................................................................................2012.7. Multi-dimension Distribution ....................................................................2013. Unconstrainted Optimization ............................................................................2113.1. Single Variable Optimization ....................................................................2113.2. Multi Variable Optimization .....................................................................2114. Constrained Optimization .................................................................................2214.1. Linear Programming..................................................................................2214.2. Quadratic Programming ............................................................................2214.3. Second Order Conic Programming............................................................2214.4. Sequential Quadratic Programming...........................................................2214.5. Integer Programming.................................................................................2215. Heuristic Optimization......................................................................................2315.1. Genetic Algorithm .....................................................................................2315.2. Simulated Annealing .................................................................................2316. Basic Statistics..................................................................................................2416.1. Mean, Variance and Covariance................................................................2416.2. Moment......................................................................................................2416.3. Rank...........................................................................................................2417. Linear Regression .............................................................................................2517.1. Least-Squares Regression..........................................................................2517.2. General Linear Least Squares....................................................................2518. Time Series Analysis ........................................................................................2618.1. Univariate Time Series..............................................................................2618.2. Multivariate Time Series ...........................................................................2618.3. ARMA .......................................................................................................2618.4. GARCH .....................................................................................................2618.5. Cointegration .............................................................................................2619. Bibliography .....................................................................................................2720. Index .....................................................................................................

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Book SynopsisGet ready to develop microservices using open source Eclipse MicroProfile and Jakarta EE, and deploy them on Kubernetes/Docker. This book covers best practices for developing cloud-native applications with MicroProfile and Jakarta EE. This book introduces you to cloud-native applications and teaches you how to set up your development environment. You'll learn about the various components of MicroProfile, such as fault tolerance, config, health check, metrics, and JWT auth. You'll develop a RESTful web service made up of some microservices. You'll deploy your application on Docker and Kubernetes. After reading this book, you'll come away with the fundamentals you need to build and deploy your first cloud-native Java-based app. What You'll LearnBuild your first cloud-native Java-based app with the open source MicroProfile platform, and Jakarta EE 10 APIsDevelop a RESTful web service using MicroProfile and Jakarta EEDiscover and explore the key components of the MicroProfile fraTable of Contents1. Introduction to Cloud-Native Applications1.1. What are Cloud-Native Applications?1.2. Why Use Cloud-Native Applications?1.3. What are the Benefits of Using Cloud-Native Applications?2. Setting Up Your Development Environment2.1. Prerequisites2.2. Installing Java SE 172.3. Installing Kubernetes2.4. Installing Docker3. Creating a Config Component3.1. What is a Config Component?3.2. Setting Up the Config Component3.3. Accessing the Configuration Data in Your Application 4. Using the Fault Tolerance Component4.1. What is the Fault Tolerance Component?4.2. Configuring the Fault Tolerance Component4.3. Handling Failures in Your Application5. Using the Health Check Component5.1. What is the Health Check Component?5.2. Configuring the Health Check Component5.3. Checking the Health of Your Application6. Using the Metrics Component6.1. What is the Metrics Component?6.2. Collecting Metrics Data in Your Application 7. Using the JWT Authentication Component7.1. What is the JWT Authentication Component?7.2. Configuring the JWT Authentication Component7.3. Authenticating Users in Your Application8. Developing a RESTful Web Service8.1. What is a RESTful Web Service?8.2. Developing a RESTful Web Service8.3. Implementing the HTTP Methods9. Deploying an Application on Kubernetes/Docker9.1. What is Kubernetes?9.2. What is Docker?9.3. Installing Kubernetes9.4. Creating a Kubernetes Cluster9.5. Installing Docker on Kubernetes9.6. Deploying an Application on Kubernetes/Docker9.7. Benefits of using Jakarta EE and Kubernetes/Docker for developing cloud-native applications.

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Book SynopsisThis book introduces reinforcement learning with mathematical theory and practical examples from quantitative finance using the TensorFlow library. Reinforcement Learning for Finance begins by describing methods for training neural networks. Next, it discusses CNN and RNN two kinds of neural networks used as deep learning networks in reinforcement learning. Further, the book dives into reinforcement learning theory, explaining the Markov decision process, value function, policy, and policy gradients, with their mathematical formulations and learning algorithms. It covers recent reinforcement learning algorithms from double deep-Q networks to twin-delayed deep deterministic policy gradients and generative adversarial networks with examples using the TensorFlow Python library. It also serves as a quick hands-on guide to TensorFlow programming, covering concepts ranging from variables and graphs to automatic differentiation, layers, models, andloss functions. After completing this boTable of ContentsChapter 1 Overview 1.1 Methods for Training Neural NetworksChapter 2 Convolutional Neural Networks 2.1 A Simple CNN 2.2 Identifying Technical Patterns in Security PricesChapter 3 Recurrent Neural Networks 3.1 LSTM Network 3.2 LSTM Application: Correlation in Asset Returns Chapter 4 Reinforcement Learning 4.1 Basics 4.2 Methods For Estimating MDP 4.3 Value Estimation Methods 4.4 Policy Learning 4.5 Actor-Critic Algorithms 4.6; Implementation of algorithms to quantitative finance using TensorFlow - 1Chapter 5 Recent Advances in Reinforcement Learning Algorithms 5.1 Double Deep Q-Network: DDQN 5.2 Dueling Double Deep Q-Network 5.3 Noisy Networks 5.4 Deterministic Policy Gradient

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Book SynopsisThis book takes developers on a journey into the cloud with Docker and Kubernetes. It walks you through the basics of Docker containers, how they are built, run, and published, and how the Kubernetes system allows you to use containers to better manage a cloud native application. Additionally, it walks you through various issues in cloud architecture, and how to design a cloud architecture that will work with your application and your team. The book takes a unique approach, getting you immersed in each subject with tutorials, then building up your technical knowledge, and finally backing up and thinking about more big-picture issues. Part one introduces Docker, building and working with Docker images, and covering best practices for Docker Containers. Part two covers the practicalities of "cloud native" and managing a Kubernetes application, including a full working example. The last part covers the design of cloud and microservice architectures, including the use of enterprise message queues, multi-site configurations and the common values that such architectures follow. This approach accelerates learning and keeps you moving forward without leaving you behind. The appendices also contain a wealth of worthwhile reference material for routine cloud application management. What You Will Learn Understand Docker and containerization Gain insight into what Kubernetes is Master essential cloud architecture design principles Design and implement notes for building cloud architectures Who This Book Is For Primarily developers who are moving to the cloud and want to get a sense of the environment they are getting into, and developers who want to move into a larger role of cloud architecture. Table of ContentsChapter ​1. Introduction - what they should expect from the book.PART 1: DockerChapter 2. Docker Under the Hood - introduction to the history and technology behind Docker. I find that really understanding Docker containers well requires a brief knowledge of their history and implementation.Chapter 3. A Docker Interactive Tutorial - the basics of building and working with Docker imagesChapter 4. Best Practices for Docker Containers - general tips, Debian vs Alpine, etc.PART 2: KubernetesChapter 5. The Cloud Native Philosophy - a general intro to the goals behind “Cloud Native” and KubernetesChapter 6. Getting Started with Kubernetes - showing how to deploy a simple system on Kubernetes with the Kubernetes dashboardChapter 7. Managing Kubernetes with kubectl - an introduction to the kubectl toolChapter 8. The Kubernetes environment - now that the user has some hands-on with Kubernetes, we introduce the components themselvesChapter 9. Basic Kubernetes Management - how to work with YAML filesChapter 10. A Full Kubernetes Cloud Example - full Kubernetes code for a cluster for a Message Board systemChapter 11. Going Further in Kubernetes - a brief introduction to other parts of KubernetesPART 3: Cloud ArchitectureChapter 12. Cloud Architecture Introduction - why architecting mattersChapter 13. Basic Cloud Architectures - basic architectural diagrams for the most common situationsChapter 14. Microservice Architectures - what microservices are, why they are important, and how to build such an architectureChapter 15. Enterprise Message Queues - what message queues are and how they make micro service architectures more flexible and resilientChapter 16. Architecting Data Stores - various database (and other data store) optionsChapter 17. Multi-Site Configurations - introducing terminology and through processes behind multi-site configurationsChapter 18. Architecture Values - a discussion of common themes for cloud architecturesChapter 19. ConclusionAppendices:1. Navigating the Linux Command Shell2. Installing Applications3. Common kubectl commands4. Kubernetes Storage Options5. Kubernetes Pod Scheduling6. Troubleshooting Kubernetes Clusters

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Book SynopsisIntermediate to AdvancedTable of Contents1. Why Spring Cloud FunctionsThis chapter takes the reader through the need for Spring Cloud Functions and KNative. The rationale for Spring Cloud Functions will be elucidated through example implementation on on-prem and cloud infrastructures. The chapter highlights the “code once deploy anywhere” characteristic of Spring Cloud Functions. Subtitles1. Writing functions and deploying to any hyperscaler2. Example code3. Spring Cloud Functions on AWS (EKS, Fargate)4. Spring Cloud Functions on Azure (AKS)5. Spring Cloud Functions on Google (Cloud Run)6. Spring Cloud Functions on VMWare Tanzu (TKG, PKS)7. Spring Cloud Functions on RedHat OpenShift (OCP)2. Getting Started with Spring Cloud FunctionsThis chapter walks the reader through the steps required to get started with Spring Serverless on their platform of choice, either locally, on-prem or on the cloud. Step by Step instructions take the reader through the process of getting the environment set up for coding.Subtitles1. Step by Step: Setup locally with Kubernetes and KNative with Spring Cloud Functions2. Step by Step: Setup on AWS with EKS and KNative with Spring Cloud Functions3. Step by Step: Setup on GCP with Cloud Run/GKE and KNative with Spring Cloud Functions4. Step by Step: Setup on Azure with AKS and KNative with Spring Cloud Functions5. Step by Step: Setup on VMWare Tanzu TKG and KNative6. Step by Step: Setup on RedHat Openshift and KNative3. Coding, testing, and deploying with Spring Cloud FunctionsThis chapter covers the coding, testing, and deploying using your favorite IDE like Eclipse, Eclipse Che, Intelij IDEA, Redhat Code Ready Workspace. The reader will build an example and deploy to their favorite platforma. Building a simple example with Spring Cloud Functionsb. Testing the example will sample datac. Setting up a CI/CD pipeline for deploying to a target platformd. Deploying to the target platfomi. AWSii. GCPiii. Azureiv. VMWare Tanzuv. RedHat Openshift4. Building Event Driven Data pipelines with Spring Cloud FunctionsEvent Driven data pipelines act as a conduit to flow of data based on a specific event. The event can be a purchase order triggered on the website that initiate a data flow chain that includes aggregation of data from various data sources and splitting the data to various consumers. This chapter will discuss the various ways that Spring Spring Serverless can be implemented in the various Cloud providersSubtitles1. Spring Cloud Functions and Spring Cloud Data Flow and Spring Cloud Streams2. Spring Cloud Functions and AWS Glue3. Spring Cloud Functions and Google Cloud Data Flow4. Spring Cloud Functions and Azure Data Factory5. AI/ML Trained Serverless Endpoints with Spring Cloud FunctionsConversational AI models are one of the complex implementations that may lead to heavy use of resources in the cloud. Leveraging Serverless infra and functions can help alleviate the costs by being invoked only when needed. This chapter will help layout the blueprint of how to leverage Spring Serverless with on-prem or cloud-based AI/ML environmentsSubtitles1. Spring Cloud Functions with Google Cloud Functions and Tensor Flow2. Spring Cloud Functions with AWS Glue and AWS Sage or AI/ML3. Spring Cloud Functions with Azure Data Factory and Azure ML4. Spring Cloud Functions with Apache AI/ML on-prem VMWare Tanzu and Openshift6. Spring Cloud Functions and IOTThis chapter will take the reader through blueprints and architect diagrams of how Spring serverless works in conjunction with IOT on various Hyperscalers (Cloud Providers) or SaaS IOT Gateway providers. Subtitles1. Spring Cloud Functions on the Cloud with AWS IOT2. Spring Cloud Functions on the cloud with Azure IOT3. Spring Cloud Functions on the cloud with GCP IOT4. Spring Cloud Functions on-prem with IOT gateway on a SaaS provider (Eg, SmartSense)7. Industry specific examples with Spring Cloud FunctionsThis chapter will provide industry specific examples and use cases that will help the reader understand how Spring Serverless can be leveraged within their specific business use case1. Oil/ Natural gas pipeline tracking with Spring Cloud Functions, IOT and Spring Cloud Data Flow2. Enabling ubiquitous health care with Spring Cloud Functions and Big Data Pipelines3. Connected vehicles with Spring Cloud Functions4. Conversational AI in Retail with Spring Cloud Functions

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Book SynopsisThis book builds upon the foundations established in its first edition, with updated chapters and the latest code implementations to bring it up to date with Tensorflow 2.0.Pro Deep Learning with TensorFlow 2.0 begins with the mathematical and core technical foundations of deep learning. Next, you will learn about convolutional neural networks, including new convolutional methods such as dilated convolution, depth-wise separable convolution, and their implementation. You''ll then gain an understanding of natural language processing in advanced network architectures such as transformers and various attention mechanisms relevant to natural language processing and neural networks in general. As you progress through the book, you''ll explore unsupervised learning frameworks that reflect the current state of deep learning methods, such as autoencoders and variational autoencoders. The final chapter covers the advanced topic of generative adversarial networks and their variaTable of ContentsChapter 1: Mathematical FoundationsChapter Goal: Setting the mathematical base for machine learning and deep learning .No of pages 100Sub -Topics1. Linear algebra 2. Calculus3. Probability4. Formulation of machine learning algorithms and optimization techniques.Chapter 2: Introduction to Deep learning Concepts and Tensorflow 2.0 Chapter Goal: Setting the foundational base for deep learning and introduction to Tensorflow 2.0 programming paradigm. No of pages: 75Sub - Topics: 5. Deep learning and its evolution.6. Evolution of the learning techniques: from perceptron based learning to back-propagation7. Different deep learning objectives functions for supervised and unsupervised learning.8. Tensorflow 2.09. GPUChapter 3: Convolutional Neural networksChapter Goal: The mathematical and technical aspects of convolutional neural networkNo of pages: 801. Convolution operation2. Analog and digital signal3. 2D and 3D convolution, dilation and depth-wise separable convolution 4. Common image processing filter 5. Convolutional neural network and components6. Backpropagation through convolution and pooling layers7. Translational invariance and equivariance 8. Batch normalization9. Image segmentation and localization methods (Moved from advanced Neural Network to here, to make room for Graph Neural Networks )Chapter 4: Deep learning for Natural Language Processing Chapter Goal: Deep learning methods and natural language processing No of pages:Sub - Topics: 1. Vector space model2. Word2Vec 3. Introduction to recurrent neural network and LSTM4. Attention 5. Transformer network architecturesChapter 5: Unsupervised Deep Learning MethodsChapter Goal: Foundations for different unsupervised deep learning techniques No of pages: 60Sub - Topics: 1. Boltzmann distribution2. Bayesian inference3. Restricted Boltzmann machines 4. Auto Encoders and variation methods Chapter 6: Advanced Neural Networks Chapter Goal: Generative adversarial networks and graph neural networks No of pages: 70Sub - Topics: 1. Introduction to generative adversarial networks 2. CycleGAN, LSGAN Wasserstein GAN3. Introduction to graph neural network4. Graph attention network and graph SAGEChapter 7: Reinforcement Learning Chapter Goal: Reinforcement Learning using Deep Learning No of pages: 50Sub - Topics: 1. Introduction to reinforcement learning and MDP formulation2. Value based methods3. DQN4. Policy based methods5. Reinforce and actor critic network in policy based formulations6. Transition-less reinforcement learning and bandit methods

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Book SynopsisThis book will teach you how to build recommender systems with machine learning algorithms using Python. Recommender systems have become an essential part of every internet-based business today.You''ll start by learning basic concepts of recommender systems, with an overview of different types of recommender engines and how they function. Next, you will see how to build recommender systems with traditional algorithms such as market basket analysis and content- and knowledge-based recommender systems with NLP. The authors then demonstrate techniques such as collaborative filtering using matrix factorization and hybrid recommender systems that incorporate both content-based and collaborative filtering techniques. This is followed by a tutorial on building machine learning-based recommender systems using clustering and classification algorithms like K-means and random forest. The last chapters cover NLP, deep learning, and graph-based techniques to build a recommender engine. EaTable of ContentsChapter 1: Introduction to Recommender SystemsChapter Goal: Introduction of recommender systems, along with a high-level overview of how recommender systems work, what are the different existing types, and how to leverage basic and advanced machine learning techniques to build these systems.No of pages: 25Sub - Topics: 1. Intro to recommender system 2. How it works3. Types and how they worka. Association rule miningb. Content basedc. Collaborative filtering d. Hybrid systemse. ML Clustering basedf. ML Classification basedg. Deep learning and NLP basedh. Graph basedChapter 2: Association Rule MiningChapter Goal: Building one of the simplest recommender systems from scratch, using association rule mining; also called market basket analysis.No of pages: 20Sub - Topics 1 APRIORI2 FP GROWTH3 Advantages and DisadvantagesChapter 3: Content and Knowledge-Based Recommender SystemChapter Goal: Building the content and knowledge-based recommender system from scratch using both product content and demographicsNo of pages: 25Sub - Topics 1 TF-IDF2 BOW3 Transformer based4 Advantages and disadvantagesChapter 4: Collaborative Filtering using KNNChapter Goal: Building the collaborative filtering using KNN from scratch, both item-item and user-user basedNo of pages: 25Sub - Topics: 1 KNN – item based2 KNN – user based3 Advantages and disadvantagesChapter 5: Collaborative Filtering Using Matrix Factorization, SVD and ALS.Chapter Goal: Building the collaborative filtering using SVM from scratch, both item-item and user-user basedNo of pages: 25Sub - Topics: 1 Latent factors2 SVD3 ALS4 Advantages and disadvantagesChapter 6: Hybrid Recommender SystemChapter Goal: Building the hybrid recommender system (Using both content and collaborative methods) which is widely used in the industryNo of pages: 25Sub - Topics: 1 Weighted: a different weight given to the recommenders of each technique used to favor some of them.2 Mixed: a single set of recommenders, without favorites.3 Augmented: suggestions from one system are used as input for the next, and so on until the last one.4 Switching: Choosing a random method5 Advantages and disadvantagesChapter 7: Clustering Algorithm-Based Recommender SystemChapter Goal: Building the clustering model for recommender systems.No of pages: 25Sub - Topics: 1 K means clustering2 Hierarchal clustering 3 Advantages and disadvantagesChapter 8: Classification Algorithm-Based Recommender SystemChapter Goal: Building the classification model for recommender systems.No of pages: 25Sub - Topics: 1 Buying propensity model2 Logistic regression3 Random forest4 SVM5 Advantages and disadvantagesChapter 9: Deep Learning and NLP Based Recommender SystemChapter Goal: Building state of art recommender system using advanced topics like Deep learning along with NLP (Natural Language processing).No of pages: 25Sub - Topics: 1 Word embedding’s2 Deep neural networks3 Advantages and disadvantagesChapter 10: Graph-Based Recommender SystemChapter Goal: Implementing graph-based recommender system using Python for computation performanceNo of pages: 25Sub - Topics: 1 Generating nodes and edges2 Building algorithm3 Advantages and disadvantagesChapter 11: Emerging Areas and Techniques in Recommender System Chapter Goal: To get an overview of the new and emerging techniques and the areas of research in Recommender systemsNo of pages: 15Sub - Topics: 1 Personalized recommendation engine2 Context-based search engine3 Multi-objective recommendations4 Summary

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Book Synopsis Chapter 1: Getting Started with Time Series.- Chapter 2: Statistical Univariate Modelling.-  Chapter 3: Statistical Multivariate Modelling.- Chapter 4: Machine Learning Regression-Based Forecasting.- Chapter 5: Forecasting Using Deep Learning. Table of ContentsChapter 1: Getting Started with Time Series.Chapter Goal: Exploring and analyzing the timeseries data, and preprocessing it, which includes feature engineering for model building.No of pages: 25Sub - Topics 1 Reading time series data2 Data cleaning3 EDA4 Trend5 Noise6 Seasonality7 Cyclicity8 Feature Engineering9 StationarityChapter 2: Statistical Univariate ModellingChapter Goal: The fundamentals of time series forecasting with the use of statistical modelling methods like AR, MA, ARMA, ARIMA, etc. No of pages: 25Sub - Topics 1 AR2 MA3 ARMA4 ARIMA5 SARIMA6 AUTO ARIMA7 FBProphetChapter 3: Statistical Multivariate ModellingChapter Goal: implementing multivariate modelling techniques like HoltsWinter and SARIMAX.No of pages: 25Sub - Topics: 1 HoltsWinter 2 ARIMAX3 SARIMAXChapter 4: Machine Learning Regression-Based Forecasting.Chapter Goal: Building and comparing multiple classical ML Regression algorithms for timeseries forecasting.No of pages: 25Sub - Topics: 1 Random Forest2 Decision Tree3 Light GBM4 XGBoost5 SVMChapter 5: Forecasting Using Deep Learning.Chapter Goal: Implementing advanced concepts like deep learning for time series forecasting from scratch.No of pages: 25Sub - Topics: 1 LSTM 2 ANN3 MLP

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