Programming and scripting languages: general Books

Book SynopsisCay S. Horstmann is the author of Modern JavaScript for the Impatient (2020), Core Java for the Impatient, Third Edition (2022), Scala for the Impatient, Second Edition (2016), and Java SE 8 for the Really Impatient (2014), all from Addison-Wesley. He has written more than a dozen other books for professional programmers and computer science students. He is an emeritus professor of computer science at San Jose State University and a Java Champion.

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Book SynopsisJames W. Cooper holds a PhD in chemistry and worked in academia, for the scientific instrument industry, and for IBM for 25 years, primarily as a computer scientist at IBM's Thomas J. Watson Research Center. Now retired, he is the author of 20 books, including 3 on design patterns in various languages. His most recent books are Flameout: The Rise and Fall of IBM Instruments (2019) and Food Myths Debunked (2014). James holds 11 patents and has written 60 columns for JavaPro Magazine. He has also written nearly 1,000 columns for the now vanished Examiner.com on foods and chemistry, and he currently writes his own blog: FoodScienceInstitute.com. Recently, he has written columns on Python for Medium.com and Substack. He is also involved in local theater groups and is the treasurer for Troupers Light Opera, where he performs regularly.Table of ContentsPreface xxi PART I: INTRODUCTION 1 The tkinter Library 2 GitHub 2 Chapter 1 Introduction to Objects 5 The Class __init__ Method 6 Variables Inside a Class 6 Collections of Classes 7 Inheritance 8 Derived Classes Created with Revised Methods 8 Multiple Inheritance 8 Drawing a Rectangle and a Square 10 Visibility of Variables 12 Properties 13 Local Variables 13 Types in Python 13 Summary 14 Programs on GitHub 15 Chapter 2 Visual Programming in Python 17 Importing Fewer Names 19 Creating an Object-Oriented Version 19 Using Message Boxes 21 Using File Dialogs 22 Understanding Options for the Pack Layout Manager 23 Using the ttk Libraries 24 Responding to User Input 25 Adding Two Numbers 26 Catching the Error 26 Applying Colors in tkinter 27 Creating Radio Buttons 27 Using a Class-Level Variable 30 Communicating Between Classes 30 Using the Grid Layout 30 Creating Checkbuttons 32 Disabling Check Boxes 32 Adding Menus to Windows 35 Using the LabelFrame 39 Moving On 40 Examples on GitHub 40 Chapter 3 Visual Programming of Tables of Data 41 Creating a Listbox 42 Displaying the State Data 44 Using a Combobox 46 The Treeview Widget 47 Inserting Tree Nodes 50 Moving On 51 Example Code on GitHub 51 Chapter 4 What Are Design Patterns? 53 Defining Design Patterns 54 The Learning Process 55 Notes on Object-Oriented Approaches 56 Python Design Patterns 57 References 57 PART II: CREATIONAL PATTERNS 59 Chapter 5 The Factory Pattern 61 How a Factory Works 61 Sample Code 62 The Two Subclasses 62 Building the Simple Factory 63 Using the Factory 63 A Simple GUI 64 Factory Patterns in Math Computation 65 Programs on GitHub 65 Thought Questions 66 Chapter 6 The Factory Method Pattern 67 The Swimmer Class 68 The Event Classes 69 Straight Seeding 70 Circle Seeding 71 Our Seeding Program 72 Other Factories 74 When to Use a Factory Method 74 Programs on GitHub 74 Chapter 7 The Abstract Factory Pattern 75 A GardenMaker Factory 75 How the User Interface Works 77 Consequences of the Abstract Factory Pattern 77 Thought Questions 78 Code on GitHub 78 Chapter 8 The Singleton Pattern 79 Throwing the Exception 80 Creating an Instance of the Class 80 Static Classes As Singleton Patterns 81 Finding the Singletons in a Large Program 81 Other Consequences of the Singleton Pattern 82 Sample Code on GitHub 82 Chapter 9 The Builder Pattern 83 An Investment Tracker 84 Calling the Builders 86 The List Box Builder 87 The Checkbox Builder 88 Displaying the Selected Securities 89 Consequences of the Builder Pattern 89 Thought Questions 89 Sample Code on GitHub 89 Chapter 10 The Prototype Pattern 91 Cloning in Python 91 Using the Prototype 92 Consequences of the Prototype Pattern 94 Sample Code on GitHub 94 Chapter 11 Summary of Creational Patterns 95 PART III: STRUCTURAL PATTERNS 97 Chapter 12 The Adapter Pattern 99 Moving Data Between Lists 99 Making an Adapter 101 The Class Adapter 103 Two-Way Adapters 103 Pluggable Adapters 103 Programs on GitHub 103 Chapter 13 The Bridge Pattern 105 Creating the User Interface 107 Extending the Bridge 108 Consequences of the Bridge Pattern 109 Programs on GitHub 110 Chapter 14 The Composite Pattern 111 An Implementation of a Composite 112 Salary Computation 112 The Employee Classes 112 The Boss Class 113 Building the Employee Tree 114 Printing the Employee Tree 114 Creating a Treeview of the Composite 116 Using Doubly Linked Lists 117 Consequences of the Composite Pattern 118 A Simple Composite 119 Other Implementation Issues 119 Dealing with Recursive Calls 119 Ordering Components 120 Caching Results 120 Programs on GitHub 120 Chapter 15 The Decorator Pattern 121 Decorating a Button 121 Using a Decorator 122 Using Nonvisual Decorators 123 Decorated Code 124 The dataclass Decorator 125 Using dataclass with Default Values 126 Decorators, Adapters, and Composites 126 Consequences of the Decorator Pattern 126 Programs on GitHub 127 Chapter 16 The Facade Pattern 129 Building the Façade Classes 131 Creating Databases and Tables 135 Using the SQLite Version 136 Consequences of the Façade 137 Programs on GitHub 137 Notes on MySQL 137 Using SQLite 138 References 138 Chapter 17 The Flyweight Pattern 139 What Are Flyweights? 139 Example Code 140 Selecting a Folder 142 Copy-on-Write Objects 143 Program on GitHub 143 Chapter 18 The Proxy Pattern 145 Using the Pillow Image Library 145 Displaying an Image Using PIL 146 Using Threads to Handle Image Loading 146 Logging from Threads 149 Copy-on-Write 149 Comparing Related Patterns 149 Programs on GitHub 150 Chapter 19 Summary of Structural Patterns 151 PART IV: BEHAVIORAL PATTERNS 153 Chapter 20 Chain of Responsibility Pattern 155 When to Use the Chain 156 Sample Code 156 The Listboxes 159 Programming a Help System 160 Receiving the Help Command 161 The First Case 162 A Chain or a Tree? 163 Kinds of Requests 164 Consequences of the Chain of Responsibility 164 Programs on GitHub 165 Chapter 21 The Command Pattern 167 When to Use the Command Pattern 167 Command Objects 168 A Keyboard Example 168 Calling the Command Objects 170 Building Command Objects 171 The Command Pattern 172 Consequences of the Command Pattern 172 Providing the Undo Function 172 Creating the Red and Blue Buttons 175 Undoing the Lines 175 Summary 176 References 176 Programs on GitHub 176 Chapter 22 The Interpreter Pattern 177 When to Use an Interpreter 177 Where the Pattern Can Be Helpful 177 A Simple Report Example 178 Interpreting the Language 179 How Parsing Works 180 Sorting Using attrgetter() 181 The Print Verb 182 The Console Interface 182 The User Interface 183 Consequences of the Interpreter Pattern 184 Programs on GitHub 185 Chapter 23 The Iterator Pattern 187 Why We Use Iterators 187 Iterators in Python 187 A Fibonacci Iterator 188 Getting the Iterator 189 Filtered Iterators 189 The Iterator Generator 191 A Fibonacci Iterator 191 Generators in Classes 192 Consequences of the Iterator Pattern 192 Programs on GitHub 193 Chapter 24 The Mediator Pattern 195 An Example System 195 Interactions Between Controls 197 Sample Code 198 Mediators and Command Objects 199 Consequences of the Mediator Pattern 200 Single Interface Mediators 200 Programs on GitHub 201 Chapter 25 The Memento Pattern 203 When to Use a Memento 203 Sample Code 204 Consequences of the Memento Pattern 209 Programs on GitHub 209 Chapter 26 The Observer Pattern 211 Example Program for Watching Colors Change 212 The Message to the Media 215 Consequences of the Observer Pattern 215 Programs on GitHub 215 Chapter 27 The State Pattern 217 Sample Code 217 Switching Between States 221 How the Mediator Interacts with the State State Transitions 224 Programs on GitHub 224 Chapter 28 The Strategy Pattern 225 Why We Use the Strategy Pattern 225 Sample Code 226 The Context 227 The Program Commands 227 The Line and Bar Graph Strategies 228 Consequences of the Strategy Pattern 230 Programs on GitHub 231 Chapter 29 The Template Pattern 233 Why We Use Template Patterns 233 Kinds of Methods in a Template Class 234 Sample Code 234 Drawing a Standard Triangle 235 Drawing an Isosceles Triangle 236 The Triangle Drawing Program 237 Templates and Callbacks 238 Summary and Consequences 238 Example Code on GitHub 238 Chapter 30 The Visitor Pattern 239 When to Use the Visitor Pattern 239 Working with the Visitor Pattern 241 Sample Code 241 Visiting Each Class 242 Visiting Several Classes 242 Bosses Are Employees, Too 243 Double Dispatching 245 Traversing a Series of Classes 245 Consequences of the Visitor Pattern 245 Example Code on GitHub 245 PART V: A BRIEF INTRODUCTION TO PYTHON 247 Chapter 31 Variables and Syntax in Python 249 Data Types 250 Numeric Constants 250 Strings 250 Character Constants 251 Variables 252 Complex Numbers 253 Integer Division 253 Multiple Equal Signs for Initialization 254 A Simple Python Program 254 Compiling and Running This Program 255 Arithmetic Operators 255 Bitwise Operators 255 Combined Arithmetic and Assignment Statements 256 Comparison Operators 256 The input Statement 257 PEP 8 Standards 258 Variable and Function Names 258 Constants 258 Class Names 258 Indentation and Spacing 259 Comments 259 Docstrings 259 String Methods 260 Examples on GitHub 261 Chapter 32 Making Decisions in Python 263 elif is “else if” 263 Combining Conditions 264 The Most Common Mistake 264 Looping Statements in Python 265 The for Loop and Lists 265 Using range in if Statements 266 Using break and continue 266 The continue Statement 267 Python Line Length 267 The print Function 267 Formatting Numbers 268 C and Java Style Formatting 269 The format string Function 269 f-string Formatting 269 Comma-Separated Numbers 270 Strings 270 Formatting Dates 271 Using the Python match Function 271 Pattern Matching 272 Reference 273 Moving On 273 Sample Code on GitHub 273 Chapter 33 Development Environments 275 IDLE 275 Thonny 275 PyCharm 276 Visual Studio 276 Other Development Environments 276 LiClipse 276 Jupyter Notebook 277 Google Colaboratory 277 Anaconda 277 Wing 278 Command-Line Execution 278 CPython, IPython, and Jython 278 Chapter 34 Python Collections and Files 279 Slicing 279 Slicing Strings 280 Negative Indexes 281 String Prefix and Suffix Removal 281 Changing List Contents 281 Copying a List 282 Reading Files 282 Using the with Loop 283 Handling Exceptions 284 Using Dictionaries 284 Combining Dictionaries 286 Using Tuples 286 Using Sets 287 Using the map Function 287 Writing a Complete Program 288 Impenetrable Coding 288 Using List Comprehension 289 Sample Programs on GitHub 290 Chapter 35 Functions 291 Returning a Tuple 292 Where Does the Program Start? 292 Summary 293 Programs on GitHub 293 Appendix A Running Python Programs 295 If You Have Python Installed 295 Shortcuts 295 Creating an Executable Python Program 296 Command-Line Arguments 297 Index 299

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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Book SynopsisThis practical, example-driven introduction is designed for Mathematica users, new and accomplished, who wish to learn the foundations of the Mathematica programming language in order to apply it to the task of solving concrete problems in science, engineering, economics and finance, computational linguistics, geoscience, bioinformatics and so on.Table of ContentsPreface; 1. An introduction to Mathematica; 2. The Mathematica language; 3. Lists; 4. Patterns and rules; 5. Functional programming; 6. Procedural programming; 7. Recursion; 8. Numerics; 9. Strings; 10. Graphics and visualization; 11. Dynamic expressions; 12. Optimizing Mathematica programs; 13. Applications and packages; References; Solutions to exercises; Index.

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Book SynopsisSince the publication of the FORTRAN 77 standard in 1978 the Fortran language has undergone a large number of revisions [61].1 The changes that were introduced reflect both new insights in programming techniques and new developments in computer hardware. From the very start the language has been designed with computing efficiency in mind. The latest standard as of this writing, Fortran 2008, puts even more emphasis on this aspect by introducing explicit support for parallel processing [71]. This first chapter gives an overview of the various standards that have appeared after FORTRAN 77. There is no attempt to be complete or even to describe all major features - that would mean a whole book or even a series of books. Consult Metcalf et al. [63], [65]or Brainerd et al. [36] for a detailed Officially, Fortran 77 should be written as FORTRAN 77. Since the Fortran 90 standard the name is written in lowercase. description of the standards. 1.1 The flavour of modern Fortran The Fortran 90 standard introduced some very significant changes with respect to the widespread FORTRAN 77 standard: free form source code, array operations, modules and derived types to name but a few. To give an impression of what this means for the programmer, consider this simple problem: We have a file with numbers, one per line (to keep it simple) and we want to determine the distribution of these numbers, that is, produce a simple histogram. In FORTRAN 77 a program that does this might look like: * Produce a simple histogram * PROGRAM HIST INTEGER MAXDATA PARAMETER (MAXDATA = 1000) INTEGER NOBND PARAMETER (NOBND = 9) REAL BOUND(NOBND) REAL DATA(MAXDATA) INTEGER I, NODATA--Trade Review'A language cannot survive without a means to learn about it. This implies the availability not only of textbooks on the language's syntax and semantics but also of books on how to use the language in real-life situations. Somehow, experience in the use and application of a language needs to be passed on to a new generation of programmers and new features require advice on how they are best to be used. Here, at a time when only rarely is a single language used in isolation, but more often in conjunction with other languages or with various tools, Modern Fortran in Practice fulfils a real need for practical advice in the field. I recommend it to all Fortran practitioners.' Michael Metcalf, from the Foreword'Scientists, mathematicians, and engineers will find that Modern Fortran in Practice speaks their language. Arjen Markus introduces modern Fortran features using real-world examples and practical advice. If your knowledge of Fortran stops at FORTRAN-77 or even Fortran 90, Markus's book will open your eyes to what today's Fortran can do for you.' Steve Lionel, Senior Member Technical Staff, Intel Corporation'Modern Fortran in Practice shows by practical examples how reliable and maintainable programs can be written. Starting with examples in Fortran 95, Fortran 2003/2008 features are then introduced and used to improve the code, thus showing different approaches but also providing a solution for older compilers. The author covers the whole development - from algorithm choice and error handling to graphical interfaces, parallelization, and unit testing. The book is an ideal complement to books that focus on the syntax of Fortran.' Tobias Burnus, Institute for Advanced Simulation, Research Centre Jülich'This book is a very welcome complement to the existing literature on modern Fortran. While most Fortran books serve as comprehensive language references, this book stands out for its extensive demonstrations of practical applications of the language. Especially interesting are the many demonstrations of the newest programming paradigms Fortran supports, including object-oriented programming, functional programming, and parallel programming.' Damian Rouson, Sandia National LaboratoryTable of Contents1. Introduction to modern Fortran; 2. Array-valued functions; 3. Mathematical abstractions; 4. Memory management; 5. An interface problem; 6. Interfacing to C: SQLite as an example; 7. Graphics, GUIs, and the internet; 8. Unit testing; 9. Code reviews; 10. Robust implementation of several simple algorithms; 11. Object-oriented programming; 12. Parallel programming; Appendix A. Tools for development and maintenance; Appendix B. Caveats.

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Book SynopsisSpecifically designed for linguists, this book provides an introduction to programming using Python for those with little to no experience of coding. Python is one of the most popular and widely-used programming languages as it''s also available for free and runs on any operating system. All examples in the text involve language data and can be adapted or used directly for language research. The text focuses on key language-related issues: searching, text manipulation, text encoding and internet data, providing an excellent resource for language research. More experienced users of Python will also benefit from the advanced chapters on graphical user interfaces and functional programming.Table of ContentsPreface; 1. Interacting with Python and basic functions; 2. Data types and variables; 3. Control structures; 4. Input-output; 5. Subroutines and modules; 6. Regular expressions; 7. Text manipulation; 8. Internet data; 9. Objects; 10. GUIs; 11. Functional programming; Appendix A. NLTK.

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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 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 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 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 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 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 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 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 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 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 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 SynopsisTable of Contents​Chapter 1. Introduction to SteamlitChapter Goal: Introduce the reader to the Streamlit libraryNo of pages - 10Sub -Topics1. A brief introduction to Streamlit2. Pre-requisites and installation guide for Streamlit3. Creating our first Streamlit application Chapter 2. Texts & Table ElementsChapter Goal: The text is one of the important features that will be discussed in this chapter.No of pages - 10Sub -Topics1. Write title, header, sub-header, markdown and a caption.2. Code text, latex and default text in an application.3. json, table, metric and dataframe in the application.Chapter 3. Charts / Visualization Chapter Goal: Visualization is one of the important aspects in data science and machine learning. The visualizing techniques helps to understand the data more appropriately. In this chapter, we will implement different visualizing techniques that are available in python for data science and machine learning developers. No of pages - 20Sub -Topics1. Implementing simple charts 2. Visualizing data using interactive charts in the application.3. Implementing data into the maps. Chapter 4. Data and Media ElementsChapter Goal: In this chapter, we will learn how media elements can be used in the streamlit application. No of pages - 20Sub -Topics1. We will first try to implement simple charts to start with and display them on the application.2. Next, we will visualize data using interactive charts in the application.3. At last, we will see how we can use data into the maps. Chapter 5. ButtonsChapter Goal: One more important feature from Streamlit are buttons. These buttons are used to select the required data to process or visualize in the application developed. No of pages - 20Sub -Topics1. Introduction to buttons2. Discuss various buttons like download button, checkbox, radio buttons and multiselect.3. Sliders to select specific range of data.Chapter 6. FormsChapter Goal: This chapter mainly focusses on data that will be provided by the user to process data in the application. We will discuss user data in terms forms. No of pages - 20Sub -Topics1. Discuss various types input data like numbers and text.2. Discuss advanced input data like date, time, file uploads and color picker. 3. Getting live image data from webcamChapter 7. NavigationsChapter Goal: This chapter discusses about navigation on the application to be developed. The primary aim is to learn how to switch between multiple pages in an application using navigation. No of pages - 20Sub -Topics1. Discuss on navigation. 2. Discuss the complex layouts associated with it.3. Discuss on containers that can be used to hold multiple elements in it.Chapter 8. Control Flow and StatusChapter Goal: We will discuss on custom handling of application using control flow in this chapter. We will also learn on status elements provided by streamlit. No of pages - 20Sub -Topics1. Handling functionality of the application using control flow. 2. Flow control of application can be changed from its default flow.3. We will also check on the what are status elements? and their types available in Streamlit.Chapter 9. Advanced FeaturesChapter Goal: In this chapter, we will discuss on huge data handling, mutating data and optimizing performance of the Streamlit application. No of pages - 20Sub -Topics1. Handling huge data in the Streamlit Application developed for data science and machine learning.2. Implementing various optimizing techniques to improve performance of the application. 3. How to mutate data in live application.Chapter 10. Project BuildChapter Goal: Finally, we will discuss to build and run complete application on various platforms. No of pages - 10Sub -Topics1. Discuss various application platforms available.2. Pre-requisites to implement developed application on these platforms.3. Implement and run the project.4. Test application on deployment and create requirement files for it.Chapter 11. Use case: NLP Project PrototypeChapter Goal: This chapter discusses about navigation on the application to be developed. The primary aim is to learn how to switch between multiple pages in an application using navigation. No of pages - 10Sub -Topics1. Pre-requisites.2. NLP module that will be implemented in our application.3. Test application after deployment.Chapter 12. Use case: Computer Vision Project PrototypeChapter Goal: We will develop a complete streamlit application on Computer Vision from scratch. We will see how all the features we have seen in the above chapters will be implemented in this application No of pages - 10Sub -Topics1. Pre-requisite.2. Computer Vision techniques that needs to implemented.3. Test all functions implemented on our deployed application.

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Book SynopsisDiscover a new way of thinking about business applications in light of the massive industry shift toward cloud computing and reactive programming technologies. This book synthesizes technologies and techniques such as event sourcing, command query responsibility segregation (CQRS), property-based testing, and GraphQL into a cohesive guide for modern business applications that benefit every developer.The book begins with a look at the fundamentals of modern business applications. These fundamentals include business rules and the managing of data over time. The benefits of reactive techniques are explained, including how they are fundamentally aligned with what application developers strive to achieve in their work.Author Peter Royal equips you with sound guidance to follow as you evolve your existing systems, as well as examples of how to build those systems using modern techniques in Spring, Java, and PostgreSQL.Table of ContentsPart I. On Business Applications1. What Is A Business Application?2. The Status Quo (and How It Can To Be)Part II. Design Prerequisites 3. What Is A Reactive System?4. Why Build Business Applications as Reactive Systems?5. What Is A Business Rule?6. Managing TimePart III. Design7. Constraints and Principles8. High-Level Data Flow9. Command Processor10. Command Generator11. Event Materializer12. Testing, Monitoring, and Observability13. Required TechnologiesPart IV. Implementation14. Building with Modern Spring, Java, and PostgreSQL15. Expansion Points and Beyond

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Book SynopsisBeginning user levelTable of ContentsChapter 1: Introduction to Precision Health and Artificial IntelligenceChapter Goal: An introduction to precision health, the concepts of AI-wearables, health data and health tech and how they transform the health industry No of pages: 15Chapter 2: Foundations of Precision HealthChapter Goal: A deep dive into precision health including key principles and processes.No of pages: 25 Chapter 3: DataChapter Goal: Data has been the beginning of many great products, services or ventures in health tech — explore types of data, and how they can be used.No of pages: 25Sub - Topics: 1. Little and big data2. Types of data3. Wearables and IoT, genomics4. Using data to enable precision health Chapter 4: Artificial Intelligence in Precision HealthChapter Goal: Concepts and ideas in artificial intelligence (AI) and machine learning -- including statistical approaches, visualization, human-computer interactions and evaluating health AI.Pages: 251. Statistical approaches2. Visualization3. Human computer interaction4. Evaluations of AIChapter 5: Ethics and RegulatoryChapter Goal: An in-depth study of legal, ethical, and regulatory concepts in precision health.No of pages: 35Sub - Topics:1.Ethics2.Legal3.Regulatory concerns Chapter 6: Case Studies: The Application of Artificial Intelligence in Precision Healthcare and MedicineChapter Goal: Applications of AI techniques and software tools. This will primarily involve exploring recent examples of AI and Machine Learning tools being specifically used to aid in clinical practice.Pages: 251. Best case examples of AI to aid clinical practice

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Book SynopsisBuild APIs and web apps based on Asynchronous Server Gateway Interface (ASGI). This book provides a comprehensive explanation of using Pydantic models to construct the request/response objects in a FASTAPI path operation.You'll start by reviewing type hints in Python and the asynchronous processing concepts. One of the highlights of FastAPI is its auto generation of API docs. Pydantic library is the main pillar on top of which FastAPI is built. You'll see that ASGI has a far better performance compared to most of the other popular Python frameworks as they implement Web server Gateway Interface (WSGI), which is synchronous in nature and hence having slower response time.This book thoroughly explains how FastAPI interacts asynchronously with relational as well as NOSQL databases. Modern web apps use template engines to interact with the front-end. In this book, you will learn to use jinja2 templates and static assets. Swagger UI and OpenAPI standards are also covered in detail. Finally,Table of ContentsBuild High Performance Web Apps with FastAPIChapter 1. Introduction to FastAPI Chapter 2. Installation of FastAPIChapter 3. Request Body Chapter 4. Templates Chapter 5. Response Chapter 6. Using Databases Chapter 7. Middleware Chapter 8. Testing and Debugging Chapter 9. Deployment Build High Performance Web Apps with FastAPI Chapter 10. Advanced Features

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Book Synopsis1. Fundamentals of RESTful APIs.- 2. Micronaut.- 3. Introduction to XML and JSON.- 4. API Design and Modeling.- 5. Introduction to JAX-RS.- 6. API Portfolio and Framework - Services.- 7. API Platform and Data Handler.- 8. API Management and CORS.Table of Contents1. Fundamentals of RESTful APIs2. Introducing Micronaut Framework3. API Design and Modeling4. Introduction to XML and JSON 5. Introduction to JAX-RS6. API Portfolio and Framework - Services7. API Platform and Data Handler8. API Management and API Client9. API Security and Caching.

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Book SynopsisLearn to write better automated tests that will dramatically increase your productivity and have fun while doing so. This book is a build-your-own adventure designed for individual reading and for collaborative workshops. You will build an xUnit automated test framework using JavaScript: initially a clone of Jest, but adding a couple of neat features borrowed from RSpec, the genre-defining tool for behavior-driven development (BDD). Along the way, you will explore the philosophy behind automated testing best practices. The automated test runner is one of the most important innovations within software engineering. But for many programmers, automated testing remains a mystery, and knowing how to write good tests is akin to sorcery. As the chapters of this book unfold, you will see how the humble test runner is an elegant and simple piece of software. Each chapter picks a single feature to build, like the it function or the beforeEach Table of ContentsPart 1: Building the Core of a Test Framework In this part we build a barebones implementation of a test runner.Chapter 1: Creating an NPM Package of My Very Own, You will create a new NPM package for concise-test, including an entry-point for the test runner.Chapter 2: Building it to Define a Testwe’ll implement the it function for defining tests, and start printing out some useful test run information on screen.Chapter 3: Grouping Tests with Describe, we’ll add support for grouping tests with the describe function, and we’ll continue to build out test reporting on screen. Chapter 4: Promoting Conciseness with BeforeEach and AfterEachWe continue our quest for concise testing facilities with the addition of beforeEach and afterEach functions. Chapter 5: Improving Legibility with Expect, We finish off the core of our API by building an abstraction over throw new Error. Part 2: Constructing a Usable Framework In this part we add features that you’ll use on a daily basis. Chapter 6: Formatting Expectation ErrorsWe write a formatter for stack traces to help pinpoint failures quickly. Chapter 7. Automatically Discovering Test FilesWe’ll add test file discovery to our test runner, and add support for running a single file through a command line argument. Chapter 8: Focusing on Tests with It.Only and Describe.Only, We split out runner into two phases: a discovery phase and an execution phase. In between them, we insert a filter phase to support running only a subset of phases. Chapter 9: Supporting Asynchronous Tests, We add the ability to wait on tests that return Promise objects, and timing out tests with it.timesOutAfter. Chapter 10: Reportingwe use a pub-sub model to build a plug-in system for reporters. Part 3: Extending for Power Users In this part we continue to add advanced features. Chapter 11: Sharing Behavior with it.BehavesLike, We borrow an important feature from Ruby: inheritance for describe blocks, which gives us a flexible mechanism for removing duplication between test groups. Chapter 12: Tagging Tests We create a mechanism for running a group of tests based on their tag. Chapter 13 : Skipping Tests we introduce a number of ways to skip tests, including tests without bodies, and it.skip plus describe.skip functions. Chapter 14 : Randomizing TestsWe add a runtime flag for randomizing the order of tests, which is a useful technique for catching invisible dependencies between tests. Part 4: Test Doubles and MocksChapter 15. Understanding test doubleswe create a function that can be used as a test spy and stub.Chapter 16. Module MocksWe create a system for mocking out entire modules, which is a useful technique when creating component doubles when testing React.

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Book SynopsisWhether you’re a beginner, switching to Java from another language, or just looking to brush up on your Java skills, this is the book you need. You’ll get a thorough grounding in the basics, including classes, objects, arrays, and exceptions. You'll also learn about more advanced topics: threads, algorithms, XML, JUnit testing, and much more.

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Book SynopsisThe Comprehensive, Expert Guide to C# Language Programming “This book has been a classic for years, and remains one of the most venerable and trusted titles in the world of C# content, and probably far beyond! . . . Mark is super smart, insists on understanding everything to the core, and has phenomenal insight into how things affect real developers. . . . He goes right to the essence and communicates with great integrity—no sugarcoating—and has a keen eye for practical value and real-world problems.” –Mads Torgersen, C# Program Manager, Microsoft Essential C# 7.0 is a well-organized, no-fluff guide to C# 7.0 for programmers at all levels of experience. Reflecting the most important C# features from 3.0 through 7.0 and including modern programming patterns, it will help you write code that’s simple, powerful, robust, secure, and maintainable. Author Mark Michaelis is a world-class C# expert: a long-time Microsoft MVP and Regional Director who also has served on Microsoft’s C# design review team. He presents a comprehensive tutorial and reference for the entire language, including expert coverage of key C# 7.0 enhancements, C# 7.0’s use with .NET Core/.NET Standard, and cross-platform compilation. He illustrates key C# constructs with succinct examples, and presents best-practice coding guidelines. To help you maintain existing code, separate indexes provide version-specific answers for C# 5.0, 6.0, and 7.0, and visual icons show when each language innovation was introduced. Make the most of C# 7.0 enhancements, including tuples, deconstructors, pattern matching, local functions, and ref returns Work efficiently with C# data types, operators, control flow, methods, and parameters Write more robust code with C# object-oriented constructs Implement reliable, effective exception handling Reduce code complexity with generics, delegates, lambda expressions, and events Leverage advanced dynamic and declarative programming techniques Query diverse data collections using LINQ with query expressions Create custom collections that operate against business objects Access .NET collections via collection interfaces and standard query operators Master multithreading and synchronization, including the async/await paradigm Optimize performance and interoperability with P/Invoke and unsafe code Run your code on Linux or macOS with C# 7.0 cross-platform compilation Includes C# 7.1, 7.2, and 7.3 language enhancements This guide offers you a complete foundation for successful development with modern versions of the C# language in any project or environment.Table of ContentsFigures xv Tables xvii Foreword xix Preface xxi Acknowledgments xxxiii About the Author xxxv Chapter 1: Introducing C# 1 Hello, World 2 C# Syntax Fundamentals 11 Working with Variables 20 Console Input and Output 24 Managed Execution and the Common Language Infrastructure 32 Multiple .NET Frameworks 37 Chapter 2: Data Types 43 Fundamental Numeric Types 44 More Fundamental Types 53 null and void 67 Conversions between Data Types 69 Chapter 3: More with Data Types 77 Categories of Types 77 Nullable Modifier 80 Tuples 83 Arrays 90 Chapter 4: Operators and Control Flow 109 Operators 110 Introducing Flow Control 126 Code Blocks ({}) 132 Code Blocks, Scopes, and Declaration Spaces 135 Boolean Expressions 137 Bitwise Operators (<<, >>, |, &, ^, ~) 147 Control Flow Statements, Continued 153 Jump Statements 165 C# Preprocessor Directives 171 Chapter 5: Methods and Parameters 181 Calling a Method 182 Declaring a Method 189 The using Directive 195 Returns and Parameters on Main() 200 Advanced Method Parameters 203 Recursion 215 Method Overloading 217 Optional Parameters 220 Basic Error Handling with Exceptions 225 Chapter 6: Classes 241 Declaring and Instantiating a Class 245 Instance Fields 249 Instance Methods 251 Using the this Keyword 252 Access Modifiers 259 Properties 261 Constructors 278 Static Members 289 Extension Methods 299 Encapsulating the Data 301 Nested Classes 304 Partial Classes 307 Chapter 7: Inheritance 313 Derivation 314 Overriding the Base Class 326 Abstract Classes 338 All Classes Derive from System.Object 344 Verifying the Underlying Type with the is Operator 345 Pattern Matching with the is Operator 346 Pattern Matching within a switch Statement 347 Conversion Using the as Operator 349 Chapter 8: Interfaces 353 Introducing Interfaces 354 Polymorphism through Interfaces 355 Interface Implementation 360 Converting between the Implementing Class and Its Interfaces 366 Interface Inheritance 366 Multiple Interface Inheritance 369 Extension Methods on Interfaces 369 Implementing Multiple Inheritance via Interfaces 371 Versioning 374 Interfaces Compared with Classes 375 Interfaces Compared with Attributes 377 Chapter 9: Value Types 379 Structs 383 Boxing 390 Enums 398 Chapter 10: Well-Formed Types 411 Overriding object Members 411 Operator Overloading 424 Referencing Other Assemblies 432 Defining Namespaces 442 XML Comments 445 Garbage Collection 449 Resource Cleanup 452 Lazy Initialization 461 Chapter 11: Exception Handling 465 Multiple Exception Types 465 Catching Exceptions 469 General Catch Block 473 Guidelines for Exception Handling 475 Defining Custom Exceptions 479 Rethrowing a Wrapped Exception 483 Chapter 12: Generics 487 C# without Generics 488 Introducing Generic Types 493 Constraints 506 Generic Methods 519 Covariance and Contravariance 524 Generic Internals 531 Chapter 13: Delegates and Lambda Expressions 537 Introducing Delegates 538 Declaring Delegate Types 542 Lambda Expressions 550 Anonymous Methods 556 Chapter 14: Events 575 Coding the Publish-Subscribe Pattern with Multicast Delegates 576 Understanding Events 591 Chapter 15: Collection Interfaces with Standard Query Operators 603 Collection Initializers 604 What Makes a Class a Collection: IEnumerable 607 Standard Query Operators 613 Anonymous Types with LINQ 646 Chapter 16: LINQ with Query Expressions 657 Introducing Query Expressions 658 Query Expressions Are Just Method Invocations 676 Chapter 17: Building Custom Collections 679 More Collection Interfaces 680 Primary Collection Classes 683 Providing an Indexer 702 Returning Null or an Empty Collection 705 Iterators 705 Chapter 18: Reflection, Attributes, and Dynamic Programming 721 Reflection 722 nameof Operator 733 Attributes 735 Programming with Dynamic Objects 759 Chapter 19: Multithreading 771 Multithreading Basics 774 Working with System.Threading 781 Asynchronous Tasks 789 Canceling a Task 810 The Task-based Asynchronous Pattern 816 Executing Loop Iterations in Parallel 846 Running LINQ Queries in Parallel 856 Chapter 20: Thread Synchronization 863 Why Synchronization? 864 Timers 893 Chapter 21: Platform Interoperability and Unsafe Code 897 Platform Invoke 898 Pointers and Addresses 910 Executing Unsafe Code via a Delegate 920 Chapter 22: The Common Language Infrastructure 923 Defining the Common Language Infrastructure 924 CLI Implementations 925 .NET Standard 928 Base Class Library 929 C# Compilation to Machine Code 929 Runtime 932 Assemblies, Manifests, and Modules 936 Common Intermediate Language 939 Common Type System 939 Common Language Specification 940 Metadata 941 .NET Native and Ahead of Time Compilation 942 Index 945 Index of 7.0 Topics 995 Index of 6.0 Topics 998 Index of 5.0 Topics 1001

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Book SynopsisReact is a JavaScript library developed for one main reason—to build reusable UI components that present ever-changing data. The React philosophy is to focus solely on the user interface. In model-viewcontroller (MVC) terminology, React is the view. Because it has to work with models and other libraries, it’s designed to play nicely with pretty much any other framework, router, style, and model library. And it's well supported—React emerged from Instagram and is now used by Facebook, Asana, Khan Academy, and Atom among many more. React Quickly is for anyone who wants to learn React.js fast. This handson book teaches needed concepts by using lots of examples, tutorials, and a large main project that gets built throughout. It starts with the basics, including how React fits into applications, JSX, and handling states, and events. Next, it explores core topics like components, forms, and data. Finally, the book dives into React integration topics, like unit testing and isomorphic JavaScript with Express.js, and Gulp. Key Features: · Uses videos to supplement learning · Chock full of examples · Gets readers using React quickly This book is for web developers who have some JavaScript experience. About the Technology: React is a JavaScript library developed for one main reason—to build reusable UI components that present ever-changing data. React emerged from Instagram and is now used by Facebook, Asana, Khan Academy, and Atom among many more.

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Book Synopsis

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Book Synopsis

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Book Synopsis

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Book Synopsis

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Book Synopsis

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Book Synopsis

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Book Synopsis

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Book Synopsis

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Book SynopsisDevelopers have made Murach''s core C# book a perennial best-seller ever since the 1st edition came out in 2004. Now, this 5th Edition is a self-paced, professional book that shows how to use Visual Studio 2012, C# 5.0, and the .NET 4.5 classes to develop Windows Forms applications. It''s an objectoriented book that shows how to use business classes, inheritance, and interfaces the way they''re used in the real world. It''s a data programming book that shows how to prototype professional database applications using RAD features like data sources, how to start using ADO.NET to work directly with databases for more control, and how to use LINQ to query a variety of data sources, from databases to arrays to XML files. It''s a book that broadens your knowledge with an introduction to Windows 8 programming. When you''re done, you''ll be able to develop 3-tiered, object-oriented Windows Forms applications the way the best professionals do.

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Book SynopsisIf you know the basics of Visual Basic, you''re ready to learn to develop server-side web applications with ASP.NET. And that''s where this book comes in: Section 1 presents a quick-start course in multi-form web applications, Visual Studio tools, and HTML5/CSS3 code -- a course that works both for beginners and for experienced web developers who are new to ASP.NET. Section 2 gives you the skills you need for every ASP.NET application you develop -- like how to use the server controls, validate user entries, manage state, use master pages and themes, provide site navigation, and use friendly URLs. Section 3 presents the skills you need for developing database-driven web sites at a professional level -- including how to use SQL data sources, object data sources, and ASP.NET data controls like the GridView, DetailsView, FormView, and ListView controls. Section 4 covers skills that enhance your professionalism; like how to develop secure web pages, authorize and authenticate users, send email, control the use of the Back button, and deploy finished applications. Section 5 takes you to the next level by showing you how to use ASP.NET Ajax, how to develop WCF and Web API services, and how ASP.NET MVC programming differs from Web Forms programming. To make it easier for you to master ASP.NET, you get complete web applications; including the web forms, the aspx code, and the VB code; that show you how each feature works in context. (You can download these for free from the Murach web site.) You get chapter exercises that let you practice your new skills. And you get Murach''s distinctive "paired-pages" format that developers like because it saves both training and reference time.

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Book SynopsisIf you want to learn how to program but do not know where to start, this is the right book and the right language for you. From the first page, our unique self-paced approach will help you build competence and confidence in your programming skills. And Python is the best language ever for learning how to program because of its simplicity and breadth two features that are hard to find in a single language. But this is not just a book for newbies! Our self-paced approach also works for experienced programmers, helping you learn faster and better than you have ever learned a language before. By the time you are through, you will have mastered all of the Python skills that are needed on the job, including those for object-oriented, database, and GUI programming. To make all of this possible, section 1 of this book presents an 8-chapter course that will get anyone off to a great start. Section 2 builds on that base by presenting the other essential skills that every Python programmer should have. Section 3 shows you how to develop object-oriented programs, a critical skillset in todays world. And section 4 shows you how to apply all of the skills that you have already learned as you build database and GUI programs for the real world.

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Book SynopsisNow in its fifth edition, bridges the gap between the technical specifications and the real world of designing and programming devices that connect over the Universal Serial Bus (USB). Readers will learn how to select the appropriate USB speed, device class, and hardware for a device; communicate with devices using Visual C#; use standard host drivers to access devices, including devices that perform vendor-defined tasks; save power with USB's built-in power-conserving protocols; and create robust designs using testing and debugging tools. This fully revised edition also includes instruction on how to increase bus speed with SuperSpeed and SuperSpeedPlus, implement wireless communications, and develop for USB On-The-Go and embedded hosts

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Book SynopsisAn invitation to travel and discover the LSP

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Book SynopsisThe text covers JSF techniques like tags, converters, validators, architecture, lifecycle, components, software installation, expression language, text field, button, custom components, RichFaces, and Ajax4Jsf app deployment.

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