Programming and scripting languages: general Books

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 Synopsis Solve SEO problems using data science. This hands-on book is packed with Python code and data science techniques to help you generate data-driven recommendations and automate the SEO workload. This book is a practical, modern introduction to data science in the SEO context using Python. With social media, mobile, changing search engine algorithms, and ever-increasing expectations of users for super web experiences, too much data is generated for an SEO professional to make sense of in spreadsheets. For any modern-day SEO professional to succeed, it is relevant to find an alternate solution, and data science equips SEOs to grasp the issue at hand and solve it. From machine learning to Natural Language Processing (NLP) techniques, Data-Driven SEO with Python provides tried and tested techniques with full explanations for solving both everyday and complex SEO problems. This book is ideal for SEO professionals who want to take their industry skiTable of ContentsData Driven SEO with PythonChapter 1: Meeting the Challenges of SEO with Data1.1 Agents of change in SEO1.2 The Pillars of SEO Strategy1.3 Installing Python1.4 Using Python for SEOChapter 2: Keyword Research2.1 Data Sources2.2 Google Search Console2.4 Google Trends2.5 Google Suggest2.6 Competitor Analytics2.7 SERPsChapter 3: Technical3.1 Improving CTRs3.2 Allocate keywords to pages based on the copy3.3 Allocating parent nodes to the orphaned URLs3.4 Improve interlinking based on copy3.5 Automate Technical AuditsChapter 4: Content & UX4.1 Content that best satisfies the user query4.2 Splitting and merging URLs4.3 Content Strategy: Planning landing page content Chapter 5: Authority5.1 A little SEO history5.1 The source of authority5.2 Finding good linksChapter 6: Competitors6.1 Defining the problem6.2 Data Strategy6.3 Data Sources6.4 Selecting Your Competitors6.5 Get Features6.6 Explore, Clean and Transform6.7 Modelling The SERPS6.8 Evaluating your Model6.9 ActivationChapter 7: Experiments7.1 How experiments fit into the SEO process7.2 Generating Hypotheses7.3 Experiment Design7.4 Running your experiment7.5 Experiment EvaluationChapter 8: Dashboards8.1 Use a Data Layer8.2 Extract, Transform and Load (ETL)8.3 Transform8.4 Querying the Data Warehouse (DW)8.5 Visualization8.6 Making Future ForecastsChapter 9: Site Migrations and Relaunches9.1 Data sources9.2 Establishing the Impact9.3 Segmenting the URLs9.4 Legacy Site URLs9.5 Priority9.6 RoadmapChapter 10: Google Updates10.1 Data sources10.2 Winners and Losers10.3 Quantifying the Impact10.4 Search Intent10.5 Unique URLs10.6 RecommendationsChapter 11: The Future of SEO11.1 Automation11.2 Your journey to SEO science11.3 Suggest resourcesAppendix: CodeGlossaryIndex

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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 SynopsisThis book explains how to scale Apache Spark 3 to handle massive amounts of data, either via batch or streaming processing. It covers how to use Spark's structured APIs to perform complex data transformations and analyses you can use to implement end-to-end analytics workflows.This book covers Spark 3's new features, theoretical foundations, and application architecture. The first section introduces the Apache Spark ecosystem as a unified engine for large scale data analytics, and shows you how to run and fine-tune your first application in Spark. The second section centers on batch processing suited to end-of-cycle processing, and data ingestion through files and databases. It explains Spark DataFrame API as well as structured and unstructured data with Apache Spark. The last section deals with scalable, high-throughput, fault-tolerant streaming processing workloads to process real-time data. Here you'll learn about Apache Spark Streaming's execution model, the architecture of Spark STable of ContentsPart I. Apache Spark Batch Data ProcessingChapter 1: Introduction to Apache Spark for Large-Scale Data Analytics1.1. What is Apache Spark? 1.2. Spark Unified Analytics1.3. Batch vs Streaming Data1.4. Spark EcosystemChapter 2: Getting Started with Apache Spark2.2. Scala and PySpark Interfaces2.3. Spark Application Concepts2.4. Transformations and Actions in Apache Spark2.5. Lazy Evaluation in Apache Spark2.6. First Application in Spark2.7. Apache Spark Web UIChapter 3: Spark Dataframe APIChapter 4: Spark Dataset APIChapter 5: Structured and Unstructured Data with Apache Spark5.1. Data Sources5.2. Generic Load/Save Functions5.3. Generic File Source Options5.4. Parquet Files5.5. ORC Files5.6. JSON Files5.7. CSV Files5.8. Text Files5.9. Hive Tables5.10. JDBC To Other DatabasesChapter 6: Spark Machine Learning with MLlibPart II. Spark Data StreamingChapter 7: Introduction to Apache Spark Streaming7.1. Apache Spark Streaming’s Execution Model7.2. Stream Processing Architectures7.3. Architecture of Spark Streaming: Discretized Streams7.4. Benefits of Discretized Stream Processing7.4.1. Dynamic Load Balancing7.4.2. Fast Failure and Straggler RecoveryChapter 8: Structured Streaming8.1. Streaming Analytics8.2. Connecting to a Stream8.3. Preparing the Data in a Stream8.4. Operations on a Streaming DatasetChapter 9: Structured Streaming Sources9.1. File Sources9.2. Apache Kafka Source9.3. A Rate SourceChapter 10: Structured Streaming Sinks10.1. Output Modes10.2. Output Sinks10.3. File Sink10.4. The Kafka Sink10.5. The Memory Sink 10.6. Streaming Table APIs10.7. Triggers10.8. Managing Streaming Queries10.9. Monitoring Streaming Queries10.9.1. Reading Metrics Interactively10.9.2. Reporting Metrics programmatically using Asynchronous APIs10.9.3. Reporting Metrics using Dropwizard10.9.4. Recovering from Failures with Checkpointing10.9.5. Recovery Semantics after Changes in a Streaming QueryChapter 11: Future Directions for Spark Streaming11.1. Backpressure11.2. Dynamic Scaling11.3. Event time and out-of-order data11.4. UI enhancements11.5. Continuous ProcessingChapter 12: Watermarks. A deep survey of temporal progress metrics

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Book Synopsis1. An Introduction to Data Analysis .- 2. Introduction to the Python's World.- 3. The NumPy Library .- 4. The pandas Library-- An Introduction.- 5. pandas: Reading and Writing Data .- 6. pandas in Depth: Data Manipulation .- 7. Data Visualization with matplotlib .- 8. Machine Learning with scikit-learn.- 9. Deep Learning with TensorFlow.- 10. An Example - Meteorological Data.- 11. Embedding the JavaScript D3 Library in IPython Notebook.- 12. Recognizing Handwritten Digits.- 13. Textual data Analysis with NLTK.- 14. Image Analysis and Computer Vision with OpenCV.- Appendix A.- Appendix B.Table of ContentsPython Data Analytics1. An Introduction to Data Analysis 2. Introduction to the Python's World3. The NumPy Library 4. The pandas Library-- An Introduction5. pandas: Reading and Writing Data 6. pandas in Depth: Data Manipulation 7. Data Visualization with matplotlib 8. Machine Learning with scikit-learn9. Deep Learning with TensorFlow10. An Example - Meteorological Data11. Embedding the JavaScript D3 Library in IPython Notebook12. Recognizing Handwritten Digits13. Textual data Analysis with NLTK 14. Image Analysis and Computer Vision with OpenCV Appendix A Appendix B

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Book SynopsisThis book is for those who wish to understand how Python debugging is and can be used to develop robust and reliable AI, machine learning, and cloud computing software. It will teach you a novel pattern-oriented approach to diagnose and debug abnormal software structure and behavior. The book begins with an introduction to the pattern-oriented software diagnostics and debugging process that, before performing Python debugging, diagnoses problems in various software artifacts such as memory dumps, traces, and logs. Next, you'll learn to use various debugging patterns through Python case studies that model abnormal software behavior. You'll also be exposed to Python debugging techniques specific to cloud native and machine learning environments and explore how recent advances in AI/ML can help in Python debugging. Over the course of the book, case studies will show you how to resolve issues around environmental problems, crashes, hangs, resource spikes, leaks, and performancedegradatioTable of ContentsChapter 1: Fundamental Vocabulary.- Chapter 2: Pattern-Oriented Debugging.- Chapter 3: Elementary Diagnostics Patterns.- Chapter 4: Debugging Analysis Patterns.- Chapter 5: Debugging Implementation Patterns.- Chapter 6: IDE Debugging in Cloud.- Chapter 7: Debugging Presentation Patterns.- Chapter 8: Debugging Architecture Patterns.- Chapter 9: Debugging Design Patterns.- Chapter 10: Debugging Usage Patterns.- Chapter 11: Case Study: Resource Leaks.- Chapter 12: Case Study: Deadlock.- Chapter 13: Challenges of Python Debugging in Cloud Computing.- Chapter 14: Challenges of Python Debugging in AI and Machine Learning.- Chapter 15: What AI and Machine Learning Can Do for Python Debugging.- Chapter 16: The List of Debugging Patterns.

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Book SynopsisMigrate from pandas and scikit-learn to PySpark to handle vast amounts of data and achieve faster data processing time. This book will show you how to make this transition by adapting your skills and leveraging the similarities in syntax, functionality, and interoperability between these tools. Distributed Machine Learning with PySpark offers a roadmap to data scientists considering transitioning from small data libraries (pandas/scikit-learn) to big data processing and machine learning with PySpark. You will learn to translate Python code from pandas/scikit-learn to PySpark to preprocess large volumes of data and build, train, test, and evaluate popular machine learning algorithms such as linear and logistic regression, decision trees, random forests, support vector machines, Naïve Bayes, and neural networks. After completing this book, you will understand the foundational concepts of data preparation and machine learning and will have the skills necessary toapply these methods using PySpark, the industry standard for building scalable ML data pipelines. What You Will LearnMaster the fundamentals of supervised learning, unsupervised learning, NLP, and recommender systemsUnderstand the differences between PySpark, scikit-learn, and pandasPerform linear regression, logistic regression, and decision tree regression with pandas, scikit-learn, and PySparkDistinguish between the pipelines of PySpark and scikit-learnWho This Book Is ForData scientists, data engineers, and machine learning practitioners who have some familiarity with Python, but who are new to distributed machine learning and the PySpark framework.Table of ContentsChapter 1: An Easy Transition.- Chapter 2: Selecting Algorithms.- Chapter 3: Multiple Linear Regression with Pandas, Scikit-Learn, and PySpark.- Chapter 4: Decision Trees for Regression with Pandas, Scikit-Learn, and PySpark.- Chapter 5: Random Forests for Regression with Pandas, Scikit-Learn, and PySpark.- Chapter 6: Gradient-Boosted Tree Regression with Pandas, Scikit-Learn and PySpark.- Chapter 7: Logistic Regression with Pandas, Scikit-Learn and PySpark.-  Chapter 8: Decision Tree Classification with Pandas, Scikit-Learn and PySpark.- Chapter 9: Random Forest Classification with Scikit-Learn and PySpark.- Chapter 10: Support Vector Machine Classification with Pandas, Scikit-Learn and PySpark.- Chapter 11: Naïve Bayes Classification with Pandas, Scikit-Learn and PySpark.- Chapter 12: Neural Network Classification with Pandas, Scikit-Learn and PySpark.- Chapter 13: Recommender Systems with Pandas, Surprise and PySpark.- Chapter 14: Natural Language Processing with Pandas, Scikit-Learn and PySpark.- Chapter 15: K-Means Clustering with Pandas, Scikit-Learn and PySpark.- Chapter 16: Hyperparameter Tuning with Scikit-Learn and PySpark.- Chapter 17: Pipelines with Scikit-Learn and PySpark.- Chapter 18: Deploying Models in Production with Scikit-Learn and PySpark.  

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Book SynopsisFinally, a book on creative programming, written directly for artists and designers! This second edition offers expanded and updated content incorporating the latest advancements and trends in the field of creative programming, also for creatives who want to work directly with P5.js and online. It delves deeper into the intricacies of computational art. Itincludes fresh case studies that explore real-world applications of coding art, inspiring readers to think beyond traditional boundaries. Rather than following a computer science curriculum, this book is aimed at creatives who are working in the intersection of design, art, and education. Following a real-world use case of computation art, you'll see how it relates back to the four key pillars, and addresses potential pitfalls and challenges in the creative process. All code examples are presented in a fully integrated Processing example library, making it easy for readers to get started.This unique and finely balanced approach betwTable of ContentsIntroductionPart I: Creative CodingIn the first main part of this book, we present a four-step working process when code is involved in art practice.Chapter 1: Idea to VisualsIn this chapter we show how creatives can translate their ideas into simple visual elements, and then work with these elements creatively, by animating them in motions and interaction. In this part, we move quickly from shaping up visual elements in static to animate and scale them in variants and complexity. We also make the comparatively large step to interaction as input for animating visual elements already in this first step. This allows creatives to directly experience the power and expressivity of a computational creative tool like Processing. We don’t hold back in this first step, because we see interaction (and ultimately data) as the important differentiator and driver behind the power of computational in creative practice. Even the raw understanding of interaction in code can help creatives ideate with minimal computation at an early stage of a project.Chapter 2: Composition and StructureIn this chapter, we align the structure of code and the visual structure to reinforce the initial momentum with transparent concepts in code. In this step, we list different code examples that are related to repetition and variation. These are all features related to the creation and manipulation of a Gestalt, introduced as the “many things as one” concept in the book. The reader learns about the shift from an individual element in repetition to many things in layers and alignment. While we introduce fewer new elements from Processing, we maintain the momentum for the reader in showing how they can, with a few lines of code, amplify the expressivity of their first tryouts. The divergence from traditional creative programming books is most visible in the second step, we leave the path of explaining Processing and let the creative process take charge.Chapter 3: Refinement and DepthIn this chapter, we use randomness and noise as sources for depth and entropy and explain how to refine and deepen the idea regarding controlling, selecting, and making choices. In this part, we specifically introduce new data structures as means to simplify and extend the design of aesthetics, rhythms and smoothness. We also return to interactivity at this developed stage and use interactivity as input to bring creative concepts closer to refinement and depth. Chapter 4: Completion and ProductionIn this chapter, we look at the backstage of coding an art piece for presentation and “show-time”. We show how to leverage the diversity of art code on other platforms like the web or in mobile applications. In this step, we also specify how to backstage, test and control the creative work in a less risky way on the final production stage.Part II: An Example: MOUNTROTHKOIn the second part of this book, we present a large example for computational art, MOUNTROTHKO (2018). This part shows how we applied many aspects of the first part of the book in a real project. We emphasize that what we write about in this book is what we also practice: we unfold MOUNTROTHKO from the very beginning by showing the conceptual and visual starting points. Then we walk through the four steps of the whole creative process in close relation to this example. In MOUNTROTHKO, certain steps were taken going back and forth, or in iteration, which underlines how the book is practically meaningful as a process reference.Chapter 5: InspirationChapter 6: From idea to completionPart III: Coding PracticeIn the last part of this book, we address common pitfalls and challenges of the creative process and formulate suggestions and tips for creatives based on our experience over the years. We indicate main problems that creatives might have in practice: how to help yourself, how to get help from others, and how to find and work with experts from other fields. Finally, we outline a continuation of the learning path for creatives alongside reading and making: this book ideally represents the starting of a personal creative journey working with both art and code.Chapter 7: Dealing with Problems Chapter 8: Learning PathChapter 9: Creative ProcessesConclusionEpilogue

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Book SynopsisLearn to implement web designs using HTML5 and CSS3. This book focuses on the need for responsive web design and how you can leverage HTML5 and CSS3 to create interactive websites that work in the real world. You do not need be a technology expert or have a prior coding background to benefit from this book. All you need is a want to learn and a curious mind to explore the full spectrum of features HTML5 and CSS3have to offer. You'll see how to migrate legacy websites to responsive websites in a very easy-to-understand, step-by-step, manner. Additionally, you will learn to use supportive software to create, run, and debug any issues that may arise during the development process. This book will also show you how to implement websites that work on multiple devices, and on various browsers such as Opera, Chrome, internet Explorer, or Edge. All this will be explored with supporting files, open-source software, as well as a practical guide to creating your demo website along with learning Table of ContentsChapter 1 Introduction to Web Development.- Chapter 2 HTML5 and Responsive Web Design.- Chapter 3 Cascading Style Sheets & Layouts.- Chapter 4 Media Queries.- Chapter 5 CSS Selectors, Color Modes & More.- Chapter 6 Animations & Transitions in CSS3.- Chapter 7 Background and Shadows in CSS.- Chapter 8 Forms with HTML.- Chapter 9 Cross Browser Challenges and How to resolve it.

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Book SynopsisDive into the diverse facets of the Laravel ecosystem. This comprehensive guide will put you on the fast track to becoming an expert in Laravel development. Starting with the fundamentals, you'll explore essential concepts, enabling you to grasp the framework's structure. Subsequent chapters cover intricate topics, such as advanced routing techniques, database management, and eloquent object relational mapping (ORM), allowing developers to build robust applications. The book offers comprehensive insights into building secure applications with authentication and authorization mechanisms, as well as constructing efficient APIs and employing caching techniques for enhanced performance. Chapters on real-time applications and queues offer practical knowledge to optimize application responsiveness. Advanced package development and performance monitoring provide valuable tools for developing high-quality Laravel packages and debugging. You'll also gain expertise in internationalization, frontTable of ContentsChapter 1: Understanding the Laravel Ecosystem.- Chapter 2: Advanced Routing Techniques.- Chapter 3: Database Management and Eloquent ORM.- Chapter 4: Authentication and Authorization.- Chapter 5: Building APIs with Laravel.- Chapter 6: Caching and Performance Optimization.- Chapter 7: Advanced Middleware Techniques.- Chapter 8: Real-Time Applications with Laravel.- Chapter 9: Testing and Test-Driven Development.- Chapter 10: Queues and Job Scheduling.- Chapter 11: Advanced Package Development.-Chapter 12: Performance Monitoring and Debugging.- Chapter 13: Scaling Laravel Applications.- Chapter 14: Security Best Practices.- Chapter 15: Advanced Performance Optimization.- Chapter 16: Continuous Integration and Deployment.- Chapter 17: Laravel and Microservices.- Chapter 18: Advanced Laravel Debugging and Troubleshooting.-Chapter 19: Building Internationalized Applications with Laravel.- Chapter 20: Advanced Frontend Development with Laravel.- Chapter 21: Advanced Error Handling and Exception Management.- Chapter 22: Advanced Database Techniques and Optimization.- Chapter 23: Laravel and Serverless Computing.- Chapter 24: Building Progressive Web Applications (PWAs) with Laravel.- Chapter 25: Advanced UI/UX Design Patterns for Laravel.- Chapter 26: Advanced Analytics and Reporting in Laravel.- Chapter 27: Building Microservices with Laravel and Docker.- Chapter 28: Advanced Third-Party Integrations.- Chapter 29: Securing Laravel Applications.- Chapter 30: Advanced DevOps and Infrastructure Automation Appendix A: Laravel Cheat Sheet .- Appendix B: Recommended Resources.- Appendix C: Glossary of Terms

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Book SynopsisComputer vision is constantly evolving, and this book has been updated to reflect new topics that have emerged in the field since the first edition's publication. All code used in the book has also been fully updated. This second edition features new material covering image manipulation practices, image segmentation, feature extraction, and object identification using real-life scenarios to help reinforce each concept. These topics are essential for building advanced computer vision applications, and you'll gain a thorough understanding of them. The book's source code has been updated from TensorFlow 1.x to 2.x, and includes step-by-step examples using both OpenCV and TensorFlow with Python. Upon completing this book, you'll have the knowledge and skills to build your own computer vision applications using neural networksWhat You Will LearnUnderstand image processing, manipulation techniques, and feature extractionmethodsWork with convolutional neural networks (CNN), single-shot deteTable of Contents

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Book SynopsisLearn to write algorithms and program in the new field of quantum computing. This second edition is updated to equip you with the latest knowledge and tools needed to be a complex problem-solver in this ever-evolving landscape. The book has expanded its coverage of current and future advancements and investments by IT companies in this emerging technology. Most chapters are thoroughly revised to incorporate the latest updates to IBM Quantum's systems and offerings, such as improved algorithms, integrating hardware advancements, software enhancements, bug fixes, and more. You'll examine quantum computing in the cloud and run experiments there on a real quantum device. Along the way you'll cover game theory with the Magic Square, an example of quantum pseudo-telepathy. You'll also learn to write code using QISKit, Python SDK, and other APIs such as QASM and execute it against simulators (local or remote) or a real quantum computer. Then peek inside the inner workings of the Bell states fTable of ContentsChapter 1: Quantum Fields - The Building Blocks of Reality Enter Max Planck, the Father of Quantum MechanicsPlanck Hits the Jackpot, Einstein collects a Novel PrizeQuantum Mechanics comes in many flavors. Which is your favorite?Many Worlds InterpretationSupplementary InterpretationsFrom Quantum Mechanics to Quantum Fields: Evolution or RevolutionChapter 1 ExercisesChapter 2: Richard Feynman, Demigod of Physics, Father of the Quantum ComputerMysteries of QFT: The Plague on InfinitiesFeynman Diagrams: Formulas in DisguiseAntimatter as Time Reverse Matter and the Mirror UniverseChapter 2 ExercisesChapter 3: The Qubit Revolution is at Hand Your Friendly Neighborhood Quantum ComputerLinear Optics vs Super Conducting LoopsThe Many Flavors of QubitChapter 3 ExercisesChapter 4: Enter the IBM Quantum: A One of a Kind Platform for Quantum Computing in the Cloud Getting your feet wet with IBM QuantumOpus 1: Variations on Bell and GHZ StatesRemote Access via the REST APIChapter 4 Exercises Chapter 5: Mathematical Foundation: Time to Dust up that Linear AlgebraQubit 101: Vector, Matrices and Complex NumbersEuler’s Identity: A Wonderful MasterpieceAlgebraic Representation of the QubitChanging the State of a Qubit with Quantum GatesUniversal Quantum Computation delivers shortcuts over Classical ComputationChapter 5 ExercisesChapter 6: QISKit, Awesome SDK for Quantum Programming in PythonInstalling the QISKitYour First Quantum ProgramQuantum Assembly: The Power behind the ScenesChapter 6 Exercises Chapter 7: Start Your Engines: From Quantum Random Numbers to Teleportation, pit stop at Super Dense CodingQuantum Random Number GenerationSuper Dense CodingQuantum TeleportationChapter 8: Game Theory: With Quantum Mechanics Odds Are Always in Your FavorCounterfeit Coin PuzzleMermin-Peres Magic SquareAnswers for the Mermin-Peres Magic Square ExerciseChapter 9: Faster Search Plus Threatening the Foundation of Asymmetric Cryptography with Grover and ShorQuantum Unstructured SearchInteger Factorization with Shor’s AlgorithmChapter 10: Advanced Algorithms for Quantum ChemistryWhat in an Eigenvalue and why should I careVariational Quantum EigensolverMolecule Ground State ExperimentProtein Folding Experiment

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Book SynopsisIn this thoroughly revised edition, author and gawk lead developer Arnold Robbins describes the awk language and gawk program in detail, shows you how to use awk and gawk for problem solving, and then dives into specific features of gawk.

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Book SynopsisIn this practical book, author Susan Fowler presents a set of microservice standards in depth, drawing from her experience standardizing over a thousand microservices at Uber. You'll learn how to design microservices that are stable, reliable, scalable, fault tolerant, performant, monitored, documented, and prepared for any catastrophe.

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Book SynopsisThis book will demystify Angular as a framework, as well as provide clear instructions and examples on how to get started with writing scalable Angular applications.

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Book SynopsisIn Think Complexity, you'll use graphs, cellular automata, and agent-based models to study topics in physics, biology, and economics. Whether you're an intermediate-level Python programmer or a student of computational modeling, you'll delve into examples of complex systems through a series of worked examples, exercises and case studies.

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Book SynopsisUpdated for the latest React release, the second edition of this hands-on guide shows you how to build React components and organize them into maintainable large-scale apps. If you're familiar with JavaScript syntax, you're ready to get started.

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Book SynopsisBuild dynamic web applications with Express, a key component of the Node/JavaScript development stack. In this updated edition, author Ethan Brown teaches you Express fundamentals by walking you through the development of an example application.

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Book SynopsisWhy reinvent the wheel every time you run into a problem with JavaScript? This cookbook is chock-full of code recipes for common programming tasks, along with techniques for building apps that work in any browser. You'll get adaptable code samples that you can add to almost any project--and you'll learn more about JavaScript in the process.

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Book SynopsisWith this practical book, Android developers will learn how to make the transition from Java to Kotlin, including how Kotlin provides a true advantage for gaining control over asynchronous computations

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Book SynopsisHow can you realize MySQL's full power? With High Performance MySQL, you'll learn advanced techniques for everything from setting service-level objectives to designing schemas, indexes, and queries to tuning your server, operating system, and hardware to achieve your platform's full potential.

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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 SynopsisPraise for the first edition:The well-written, comprehensive book[is] aiming to become a de facto reference for the language and its features and capabilities. The pace is appropriate for beginners; programming concepts are introduced progressively through a range of examples and then used as tools for building applications in various domains, including sophisticated data structures and algorithmsHighly recommended. Students of all levels, faculty, and professionals/practitioners.D. Papamichail, University of Miami in CHOICE Magazine Mark Lewis' Introduction to the Art of Programming Using Scala was the first textbook to use Scala for introductory CS courses. Fully revised and expanded, the new edition of this popular text has been divided into two books. Introduction to Programming and Problem-Solving Using Scala is designed to be used in first semester college classrooms to teach students beginning programming with Scala. TheTable of ContentsBasics of Computers, Computing, and Programming. Getting to Know the Tools. Scala Basics. Conditionals. Functions. Recursion for Iteration. Arrays and Lists in Scala. Loops. Text Files. Case Classes. GUIs. Graphics. Sorting and Searching. XML. Recursion.

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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 SynopsisBecome a Python programmer–and have fun doing it! Start writing software that solves real problems, even if you have absolutely no programming experience! This friendly, easy, full-color book puts you in total control of your own learning, empowering you to build unique and useful programs. Microsoft has completely reinvented the beginning programmer’s tutorial, reflecting deep research into how today’s beginners learn, and why other books fall short. Begin to Code with Python is packed with innovations, from its “Snaps” prebuilt operations to its “Make Something Happen” projects. Whether you’re a total beginner or you’ve tried before, this guide will put the power, excitement, and fun of programming where it belongs: in your hands! Easy, friendly, and you’re in control! Learn how to... Get, install, and use powerful free tools to create modern Python programs Learn key concepts from 170 sample programs, and use them to jumpstart your own Discover exactly what happens when a program runs Approach program development with a professional perspective Learn the core elements of the Python language Build more complex software with classes, methods, and objects Organize programs so they’re easy to build and improve Capture and respond to user input Store and manipulate many types of real-world data Define custom data types to solve specific problems Create interactive games that are fun to play Build modern web and cloud-based applications Use pre-built libraries to quickly create powerful software Get code samples, including complete apps, at: https://aka.ms/BegintoCodePython/downloads About This Book For absolute beginners who’ve never written a line of code For anyone who’s been frustrated with other beginning programming books or courses For people who’ve started out with other languages and now want to learn Python Works with Windows PC, Apple Mac, Linux PC, or Raspberry Pi Includes mapping of MTA exam objectives that are covered in this book, as well as an appendix with further explanation of some of the topics on the exam Table of ContentsPart 1: Programming fundamentals Chapter 1 Starting with Python Chapter 2 Python and Programming Chapter 3 Python program structure Chapter 4 Working with variables Chapter 5 Making decisions in programs Chapter 6 Repeating actions with loops Chapter 7 Using functions to simplify programs Chapter 8 Storing collections of data Part 2: Advanced programming Chapter 9 Use classes to store data Chapter 10 Use classes to create active objects Chapter 11 Object-based solution design Chapter 12 Python applications Part 3: Useful Python (Digital-only) Chapter 13 Python and Graphical User Interfaces Chapter 14 Python programs as network clients Chapter 15 Python programs as network servers Chapter 16 Create games with pygame

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