Web programming Books

Book SynopsisMachine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions.

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Book SynopsisDescription All aspects of software development and deployment become painfully slow. The solution is to adopt the microservice architecture. This architecture accelerates software development and enables continuous delivery and deployment of complex software applications. Microservice Patterns teaches enterprise developers and architects how to build applications with the microservice architecture. This book also teaches readers how to refactor a monolithic application to a microservice architecture. Key features · In-depth guide · Practical examples · Step-by-step instructions Audience Readers should be familiar with the basics of enterprise application architecture, design, and implementation. About the technology Microservice architecture accelerates software development and enables continuous delivery and deployment of complex software applications. Author biography Chris Richardson is a developer and architect. He is a Java Champion, a JavaOne rock star and the author of POJOs in Action, which describes how to build enterprise Java applications with frameworks such as Spring and Hibernate. Chris was also the founder of the original CloudFoundry.com, an early Java PaaS for Amazon EC2. Today, he is a recognized thought leader in microservices. Chris is the creator of http://microservices.io , a website describing how to develop and deploy microservices. He provides microservices consulting and training and is working on his third startup http://eventuate.io , an application platform for developing microservices.Trade Review'A comprehensive overview of the challenges teams face when moving to microservices, with industry-tested solutions to these problems.' Tim Moore, Lightbend 'Pragmatic treatment of an important new architectural landscape.' Simeon Leyzerzon, Excelsior Software 'A solid compendium of information that will quicken your migration to this modern cloud-based architecture.' John Guthrie, Dell/EMC 'How to understand the microservices approach, and how to use it in real life.' Potito Coluccelli, Bizmatica EconocomTable of Contentstable of contents READ IN LIVEBOOK 1.ESCAPING MONOLITHIC HELL READ IN LIVEBOOK 2.DECOMPOSITION STRATEGIES READ IN LIVEBOOK 3.INTER-PROCESS COMMUNICATION IN A MICROSERVICE ARCHITECTURE READ IN LIVEBOOK 4.MANAGING TRANSACTIONS WITH SAGAS READ IN LIVEBOOK 5.DESIGNING BUSINESS LOGIC IN A MICROSERVICE ARCHITECTURE READ IN LIVEBOOK 6.DEVELOPING BUSINESS LOGIC WITH EVENT SOURCING READ IN LIVEBOOK 7.IMPLEMENTING QUERIES IN A MICROSERVICE ARCHITECTURE READ IN LIVEBOOK 8.EXTERNAL API PATTERNS READ IN LIVEBOOK 9.TESTING MICROSERVICES - PART 1 READ IN LIVEBOOK 10.TESTING MICROSERVICES - PART 2 READ IN LIVEBOOK 11.DEVELOPING PRODUCTION READY SERVICES READ IN LIVEBOOK 12.DEPLOYING MICROSERVICES READ IN LIVEBOOK 13.REFACTORING TO MICROSERVICES

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Book Synopsis100 Go Mistakes: How to Avoid Them introduces dozens of techniques for writing idiomatic, expressive, and efficient Go code that avoids common pitfalls. By reviewing dozens of interesting, readable examples and real-world case studies, you'll explore mistakes that even experienced Go programmers make. This book is focused on pure Go code, with standards you can apply to any kind of project. As you go, you'll navigate the tricky bits of handling JSON data and HTTP services, discover best practices for Go code organization, and learn how to use slices efficiently. Your code speed and quality will enjoy a huge boost when you improve your concurrency skills, deal with error management idiomatically, and increase the quality of your tests. About the Technology Go is simple to learn, yet hard to master. Even experienced Go developers may end up introducing bugs and inefficiencies into their code. This book accelerates your understanding of Go's quirks, helping you correct mistakes and dodge pitfalls on your path to Go mastery.Trade Review"This book is one any Golang developer will want on their bookshelf. Far from being dogmatic or prescriptive, it often provides multiple solutions to the reader, leaving some room for flexibility and individual taste." Thad Meyer "Goes beyond the basics with lots of good examples for when concepts are tough to grasp. As someone who's been coding Go for about 2 years, I learned new things." Matt Welke "This book felt catered to me. I'm not a developer by career path, however it provides a LOT of insight into what I should be thinking about as someone without any education or formal training in Software Development. Really, really nice." Francis J. Setash "This book not only points out common mistakes and anti-patterns, it provides solutions—a perfect combination for deeper learning." Kevin Liao "Read this, it'll give you years of experience of Go just learning from the book. Very valuable!" Keith Kim

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Book SynopsisRuss Unger is a leader in experience design, known for building dynamic teams across sectors such as enterprise, government, and private organizations. He is the co-author of the books Liftoff! Practical Design Leadership to Elevate Your Team, Your Organization, and You, published by Rosenfeld Media, as well as A Project Guide to UX Design, Designing the Conversation, and Speaker Camp (Voices that Matter). Carolyn Chandler is a user experience strategist and has been leading experience design teams for over 25 years. She has developed and taught UX design classes at DePaul University, Northwestern University's Kellogg School of Management, and a variety of other programs tailored to those who want to create impactful digital products. She is also coauthor of Adventures in Experience Design (Voices That Matter).

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Book SynopsisTable of ContentsForeword by Vaughn Vernon, Series Editor xvii Foreword by Frank Leymann xxi Preface xxiii Part 1: Foundations and Narratives 1 Chapter 1: Application Programming Interface (API) Fundamentals 3 From Local Interfaces to Remote APIs 3 Decision Drivers in API Design 14 A Domain Model for Remote APIs 22 Summary 28 Chapter 2: Lakeside Mutual Case Study 31 Business Context and Requirements 31 Architecture Overview 35 API Design Activities 39 Target API Specification 39 Summary 41 Chapter 3: API Decision Narratives 43 Prelude: Patterns as Decision Options, Forces as Decision Criteria 43 Foundational API Decisions and Patterns 45 Decisions about API Roles and Responsibilities 57 Selecting Message Representation Patterns 70 Interlude: Responsibility and Structure Patterns in the Lakeside Mutual Case 82 Governing API Quality 84 Deciding for API Quality Improvements 98 Decisions about API Evolution 110 Summary 122 Part 2: The Patterns 125 Chapter 4: Pattern Language Introduction 127 Positioning and Scope 128 Patterns: Why and How? 130 Navigating through the Patterns 131 Foundations: API Visibility and Integration Types 137 Basic Structure Patterns 146 Summary 158 Chapter 5: Define Endpoint Types and Operations 161 Introduction to API Roles and Responsibilities 162 Endpoint Roles (aka Service Granularity) 167 Operation Responsibilities 215 Summary 248 Chapter 6: Design Request and Response Message Representations 253 Introduction to Message Representation Design 253 Element Stereotypes 256 Special-Purpose Representations 282 Summary 305 Chapter 7: Refine Message Design for Quality 309 Introduction to API Quality 309 Message Granularity 313 Client-Driven Message Content (aka Response Shaping) 325 Message Exchange Optimization (aka Conversation Efficiency) 344 Summary 355 Chapter 8: Evolve APIs 357 Introduction to API Evolution 357 Versioning and Compatibility Management 362 Life-Cycle Management Guarantees 374 Summary 393 Chapter 9: Document and Communicate API Contracts 395 Introduction to API Documentation 395 Documentation Patterns 398 Summary 421 Part 3: Our Patterns in Action (Now and Then) 423 Chapter 10: Real-World Pattern Stories 425 Large-Scale Process Integration in the Swiss Mortgage Business 426 Offering and Ordering Processes in Building Construction 438 Summary 445 Chapter 11: Conclusion 447 Short Retrospective 448 API Research: Refactoring to Patterns, MDSL, and More 449 The Future of APIs 450 Additional Resources 451 Final Remarks 451 Appendix A: Endpoint Identification and Pattern Selection Guides 453 Appendix B: Implementation of the Lakeside Mutual Case 463 Appendix C: Microservice Domain-Specific Language (MDSL) 471 Bibliography 483 Index 499

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Book SynopsisExplores the platforms that you can use to develop hardware or software, discusses design concepts that can make your products eye-catching and appealing. This book explains how to combine sensors, servos, robotics, Arduino chips, and more with various networks or the Internet, to create interactive, cutting-edge devices.Trade ReviewAccording to friends of mine who work in the disciplines above, this is an excellent introduction to read through the principles of prototyping through to manufacture and business considerations (Mob76 Outlook, December 2013)Table of ContentsIntroduction 1 PART I: PROTOTYPING 5 Chapter 1: The Internet of Things: An Overview 7 Chapter 2: Design Principles for Connected Devices 21 Chapter 3: Internet Principles 41 Chapter 4: Thinking About Prototyping 63 Chapter 5: Prototyping Embedded Devices 87 Chapter 6: Prototyping the Physical Design 147 Chapter 7: Prototyping Online Components 173 Chapter 8: Techniques for Writing Embedded Code 205 PART II: FROM PROTOTYPE TO REALITY 225 Chapter 9: Business Models 227 Chapter 10: Moving to Manufacture 255 Chapter 11: Ethics 289 Index 311

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Book SynopsisWhen it comes to data analytics, it pays tothink big. PySpark blends the powerful Spark big data processing engine withthe Python programming language to provide a data analysis platform that can scaleup for nearly any task. Data Analysis with Python and PySpark is yourguide to delivering successful Python-driven data projects. Data Analysis with Python and PySpark is a carefully engineered tutorial that helps you use PySpark to deliver your data-driven applications at any scale. This clear and hands-on guide shows you how to enlarge your processing capabilities across multiple machines with data from any source, ranging from Had oop-based clusters to Excel worksheets. You'll learn how to break down big analysis tasks into manageable chunks and how to choose and use the best PySpark data abstraction for your unique needs. The Spark data processing engine is an amazing analytics factory: raw data comes in,and insight comes out. Thanks to its ability to handle massive amounts of data distributed across a cluster, Spark has been adopted as standard by organizations both big and small. PySpark, which wraps the core Spark engine with a Python-based API, puts Spark-based data pipelines in the hands of programmers and data scientists working with the Python programming language. PySpark simplifies Spark's steep learning curve, and provides a seamless bridge between Spark and an ecosystem of Python-based data science tools. Trade Review“A great and gentle introduction to spark.” Javier Collado Cabeza “A phenomenal introduction to PySpark from the ground up.”Anonymous Reviewer “A great book to get you started with PySpark!” Jeremy Loscheider “Takes you on an example focused tour of building pyspark data structures from the data you provide and processing them at speed.” Alex Lucas “If you need to learn PySpark (as a Data Scientist or Data Wrangler) start with this book!”Geoff Clark

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Book SynopsisModern software systems are composed of many servers, services, and other components that communicate through APIs. As a developer, your job is to make sure these APIs are stable, reliable, and easy to use for other developers. API Design Patterns provides you with a unique catalog of design standards and best practices to ensure your APIs are flexible and user-friendly. Fully illustrated with examples and relevant use-cases, this essential guide covers patterns for API fundamentals and real-world system designs, along with quite a few not-so-common scenarios and edge-cases. about the technologyAPI design patterns are a useful set of best practice specifications and common solutions to API design challenges. Using accepted design patterns creates a shared language amongst developers who create and consume APIs, which is especially critical given the explosion of mission-critical public-facing web APIs. API Patterns are still being developed and discovered. This collection, gathered and tested by Google API expert JJ Geewax, is the first of its kind. about the book API Design Patterns draws on the collected wisdom of the API community, including the internal developer knowledge base at Google, laying out an innovative set of design patterns for developing both internal and public-facing APIs. In this essential guide, Google Software Engineer JJ Geewax provides a unique and authoritative catalog of patterns that promote flexibility and ease-of-use in your APIs. Each pattern in the catalog is fully illustrated with its own example API, use-cases for solving common API design challenges, and scenarios for tricky edge issues using a pattern’s more subtle features. With the best practices laid out in this book, you can ensure your APIs are adaptive in the face of change and easy for your clients to incorporate into their projects. what's inside A full case-study of building an API and adding features The guiding principles that underpin most API patterns Fundamental patterns for resource layout and naming Advanced patterns for special interactions and data transformations about the readerAimed at software developers with experience using APIs, who want to start building their own. about the author JJ Geewax is a software engineer at Google, focusing on Google Cloud Platform and API design. He is also the author of Google Cloud Platform in Action.

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Book Synopsis Joshua Bloch is a professor at Carnegie Mellon University. He was formerly the chief Java architect at Google, a distinguished engineer at Sun Microsystems, and a senior systems designer at Transarc. He led the design and implementation of numerous Java platform features, including the JDK 5.0 language enhancements and the Java Collections Framework. He holds a Ph.D. in computer science from Carnegie Mellon University and a B.S. in computer science from Columbia University. Table of Contents Chapter 1: Introduction Chapter 2: Creating and Destroying Objects Chapter 3: Methods Common to All Objects Chapter 4: Classes and Interfaces Chapter 5: Generics Chapter 6: Enums and Annotations Chapter 7: Lambdas and Streams Chapter 8: Methods Chapter 9: General Programming Chapter 10: Exceptions Chapter 11: Concurrency Chapter 12: Serialization References Index

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Book SynopsisDescription Modern NLP techniques based on machine learning radically improve the ability of software to recognize patterns, use context to infer meaning, and accurately discern intent from poorly-structured text. In Natural Language Processing in Action, readers explore carefully chosen examples and expand their machine's knowledge which they can then apply to a range of challenges. Key Features • Easy-to-follow • Clear examples • Hands-on-guide Audience A basic understanding of machine learning and some experience with a modern programming language such as Python, Java, C++, or JavaScript will be helpful. About the technology Natural Language Processing (NLP) is the discipline of teaching computers to read more like people, and readers can see examples of it in everything from chatbots to the speech-recognition software on their phone. Hobson Lane has more than 15 years of experience building autonomous systems that make important decisions on behalf of humans. Hannes Hapke is an Electrical Engineer turned Data Scientist with experience in deep learning. Cole Howard is a carpenter and writer turned Deep Learning expert.

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Book SynopsisDescription Manning's bestselling Java 8 book has been revised for Java 9! In Java 8 and 9 in Action, readers build on their existing Java language skills with the newest features and techniques. The release of Java 9 builds on what made Java 8 so exciting. In addition to Java 8's lambdas and streams, Java 9 adds a host of new features of its own. It includes new library features to support reactive programming, which give users new ways of thinking about programming and writing code that is easier to read and maintain. Key Features · Contains all of Java 9’s new features · The Java Module System · Testing and debugging with lambdas Audience This book is written for programmers familiar with Java and basic OO programming. About the Technology Java 9 introduces the long-awaited Java Module System. Modules encourage users to write their code in smaller units that are easier to test, manage and release. Java 9 also helps programmers by enriching the functional-programming and streams features of Java 8.

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Book SynopsisPython's simplicity lets you become productive quickly, but often this means you aren't using everything the language has to offer. With the updated edition of this hands-on guide, you'll learn how to write effective, modern Python 3 code by leveraging its best ideas. Discover and apply idiomatic Python 3 features beyond your past experience.

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Book SynopsisWith this practical guide, you'll learn what it takes to design usable REST APIs that evolve over time. By focusing on solutions that cross a variety of domains, this book shows you how to create powerful and secure applications, using the tools designed for the world's most successful distributed computing system: the World Wide Web.

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Book SynopsisDESCRIPTIONDeep learning is applicable to a widening range of artificialintelligence problems, such as image classification, speech recognition,text classification, question answering, text-to-speech, and opticalcharacter recognition. Deep Learning with Python is structured around a series of practicalcode examples that illustrate each new concept introduced anddemonstrate best practices. By the time you reach the end of this book,you will have become a Keras expert and will be able to apply deeplearning in your own projects. KEY FEATURES • Practical code examples• In-depth introduction to Keras• Teaches the difference between Deep Learning and AI ABOUT THE TECHNOLOGYDeep learning is the technology behind photo tagging systems atFacebook and Google, self-driving cars, speech recognition systems onyour smartphone, and much more. AUTHOR BIOFrancois Chollet is the author of Keras, one of the most widely usedlibraries for deep learning in Python. He has been working with deep neuralnetworks since 2012. Francois is currently doing deep learning research atGoogle. He blogs about deep learning at blog.keras.io.Trade Review‘...is focused, concise and precise. It provides express and effective revision material and techniques without compromising the depth of your understanding.' Avis Whyte, Senior Research Fellow, University of Westminster ‘An accessible quick revision guide with all the essential information in one place which makes a good addition to textbooks and other study material.' J oanne Atkinson, Director of Postgraduate Law Programmes, University of Portsmouth ‘... excellent companion for students. It is to be used as a revision guide and will be useful for students who are conversant with the principles and case law of each topic.' Alison Poole, Teaching Fellow, University of Portsmouth 'This series is great - after having revised everything, it showed me a way to condense all the information and gave me an idea of how I would go about structuring my essays.' Arama Lemon, Student, Coventry University ‘The Law Express Q&A series is perfect as it targets different learning styles - it includes diagrams and flowcharts that you can follow for easy application with confidence. It's perfect for anyone who wants to receive an extra boost with their revision!' Mariam Hussain, Student, University of Westminster

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Book SynopsisOffers an introduction to Natural Language Processing, the field that underpins a variety of language technologies, ranging from predictive text and email filtering to automatic summarization and translation. This book helps you learn how to write Python programs to work with large collections of unstructured text.

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Book SynopsisDescription With Kubernetes, users don't have to worry about which specific machine in their data center their application is running on. Each layer in their application is decoupled from other layers so they can scale, update, and maintain them independently. Kubernetes in Action teaches developers how to use Kubernetes to deploy self-healing scalable distributed applications. By the end, readers will be able to build and deploy applications in a proper way to take full advantage of the Kubernetes platform. Key features • Easy to follow guide • Hands-on examples • Clearly-written Audience The book is for both application developers as well as system administrators who want to learn about Kubernetes from the developer’s perspective. About the Technology Kubernetes abstracts away the hardware infrastructure and exposes your whole datacenter as a single enormous computational resource.

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Book SynopsisWith lots of examples and practical advice, the second edition of this practical book takes a holistic view of the topics that system architects and administrators must consider when building, managing, and evolving microservice architectures.

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Book SynopsisClassic Computer Science Problems in Python presents dozens of coding challenges, ranging from simple tasks like finding items in a list with a binary sort algorithm to clustering data using k-means. Classic Computer Science Problems in Python deepens your Python language skills by challenging you with time-tested scenarios, exercises, and algorithms. As you work through examples in search, clustering, graphs, and more, you'll remember important things you've forgotten and discover classic solutions to your "new" problems Key Features · Breadth-first and depth-first search algorithms · Constraints satisfaction problems · Common techniques for graphs · Adversarial Search · Neural networks and genetic algorithms · Written for data engineers and scientists with experience using Python. For readers comfortable with the basics of Python About the technology Python is used everywhere for web applications, data munging, and powerful machine learning applications. Even problems that seem new or unique stand on the shoulders of classic algorithms, coding techniques, and engineering principles. Master these core skills, and you’ll be ready to use Python for AI, data-centric programming, deep learning, and the other challenges you’ll face as you grow your skill as a programmer. David Kopec teaches at Champlain College in Burlington, VT and is the author of Manning’s Classic Computer Science Problemsin Swift.

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Book SynopsisFor nearly 25 years this best seller has been the go-to guide for JavaScript programmers. The seventh edition is fully updated to cover the 2020 version of JavaScript, and new chapters cover classes, modules, iterators, generators, Promises, async/await, and metaprogramming.

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Book SynopsisBuild your own video chat application - but that's just the beginning. With WebRTC, you'll create real-time applications to stream any kind of user media and data directly from one browser to another, all built on familiar HTML, CSS, and JavaScript. Power real-time activities like text-based chats, secure peer-to-peer file transfers, collaborative brainstorming sessions - even multiplayer gaming. And you're not limited to two connected users: an entire chapter of the book is devoted to engineering multipeer WebRTC apps that let groups of people communicate in real time. You'll create your own video conferencing app. It's all here. WebRTC is an API exposed in all modern web browsers. After almost a decade of development, the WebRTC specification was finalized, and this book provides faithful coverage of that finalized specification. You'll start by building a basic but complete WebRTC application for video chatting. Chapter by chapter, you'll refine that app and its core logic to spin up new and exciting WebRTC-powered apps that will have your users sharing all manner of data with one another, all in real time. No third-party libraries or heavy downloads are required for you or your users: you'll be writing and strengthening your knowledge of vanilla JavaScript and native browser APIs. You'll learn how to directly connect multiple browsers over the open internet using a signaling channel. You will gain familiarity with a whole set of Web APIs whose features bring WebRTC to life: requesting access to users' cameras and microphones; accessing and manipulating arbitrary user files, right in the browser; and web storage for persisting shared data over the life of a WebRTC call. Like any Web API, WebRTC doesn't enjoy a perfect implementation in any browser. But this book will guide you in writing elegant code to the specification, with backward-compatible fallback code for use in almost all modern browsers. Use WebRTC to build the next generation of web applications that stream media and data in real time, directly from one user to another - all by working in the browser. What You Need: Readers need a text editor, an up-to-date copy of Chrome or Firefox, and a POSIX-style command-line shell. They'll also need to install a little bit of open-source software, especially Node.js. All necessary setup is covered in full in the book's introductory chapter.

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Book SynopsisLoren Klingman is a full-stack web developer and instructor at Big Nerd Ranch. He has over 15 years of experience across a variety of technologies. When he's not at work, he can be found playing tabletop games. Ashley Parker is an engineering team manager and instructor at Big Nerd Ranch, where she loves to learn new things. She specializes in front-end web development, with a focus on React and React Native. When she's not in front of a computer, you can find her reading, traveling, or doing mom things.Table of ContentsIntroduction The Necessary Tools Create React App Components User Events State Linting Prop Types Styles Interacting with a Server Router Conditional Rendering useReducer Editing the Cart Forms Local Storage and useRef Submitting Orders Component Composition Context Fulfilling Orders Introduction to App Performance Optimization Testing Overview Testing with Jest and the React Testing Library End-to-End Testing Building Your Application Data Loading Component Speed Afterword

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Book SynopsisIf you understand the basics of HTML5, CSS, and JavaScript and want to make quick sense of the extensive but often overwhelming reference documentation on D3.js, this short book is for you.

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Book SynopsisYou're a student who wants to jumpstart their career with practical skills, or you're a self-taught beginner who has learned all you can from beginner programmer books and coding bootcamps. Now you're looking for the next step to becoming a real-world professional programmer so you can create your own apps and get started with your career. If that fits, then this book is for you! This book is perfect for self-taught programmers looking for the stuff intro books don't teach you and students wanting to get practical information before getting started with applying their new programming skills.Trade Review"A great new book . . . Sweigart focuses on three major subjects: common difficulties in getting started (seeking help, setting up a work environment); best practices, tools, and techniques; and using object-oriented Python. The second section is the largest in the book . . . but each section can be read on its own. The book is all the more useful for collecting together between one pair of covers material that you would typically dig up from multiple resources." —Serdar Yegulalp, InfoWorld"My early Python programs work but could be improved massively with what Al writes about . . . a small goldmine of knowledge that beginners, intermediates and probably even advanced programmers will benefit from." —GeekTechStuffTable of ContentsIntroductionPart I: Getting Started1. Dealing with Errors and Asking for Help2. Environmental Setup3. Formatting with the Black Module Part II: Best Practices, Tools, and Techniques4. Choosing Useful Names5. Finding Code Smells6. Writing Pythonic Code7. Programming Jargon8. Common Python Gotchas9. Esoteric Python Oddities10. Crafting Effective Functions11. Comments and Type Hints12. Version Control with Git13. Profiling Code Part III: Practice Problems14. Tower of Hanoi15. Connect Four Part IV: Readable Object-Oriented Programming16. Classes and Objects17. Inheritance18. Pythonic Object-Oriented Programming Index

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Book SynopsisThis guide, collaboratively written by over a hundred members of the Python community, describes best practices currently used by package and application developers. Unlike other books for this audience, The Hitchhiker's Guide is light on reusable code and heavier on design philosophy, directing the reader to excellent sources that already exist.

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Book SynopsisMike Atherton is a content strategist at Facebook. He has over 20 years of experience designing digital products and the teams who create them. Carrie Hane is the founder of Tanzen, which provides content strategy consulting and training. For 20 years, she's been helping organizations and people rethink how they create, manage, and connect content.Table of ContentsLET’S GET CONNECTED 1 Designing From the Bottom Up 2 Why We Need a New Way of Approaching Digital Content 3 Understanding Structured Content STRUCTURING CONTENT 4 Researching the Subject Domain 5 Creating a Domain Model 6 Translating to a Content Model PUBLISHING CONTENT 7 Designing Connected Content 8 Implementing Connected Content 9 Bringing Your Content to Life THE FUTURE 10 The Future Isn’t Waiting

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Book SynopsisIf you want to learn how to build efficient React applications, this is your book. Ideal for web developers and software engineers who understand how JavaScript, CSS, and HTML work in the browser, this updated edition provides best practices and patterns for writing modern React code.

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Book SynopsisThe kid-friendly way to learning coding with Python Calling all wanna-be coders! Experts point to Python as one of the best languages to start with when you're learning coding, and Python For Kids For Dummies makes it easier than ever.Table of ContentsIntroduction 1 Week 1: Slithering into Python 7 Project 1: Getting Started with Python 9 Project 2: Hello World! 35 Week 2: Building Guessing Games 57 Project 3: Guessing Game 59 Project 4: Set Up Your Coding Environment 84 Project 5: A Better Guessing Game 103 Week 3: Creating Word Games 141 Project 6: Hacker Speaker: 1337 Sp34k3r 143 Project 7: Cryptopy 177 Project 8: Silly Sentences 219 Week 4: Stepping Up to Object]Oriented Programming 235 Project 9: Address Book 237 Project 10: Math Trainer 281 Index 309

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Book SynopsisCovers the essentials first-time XSLT users need to know about creating basic style sheets, working with various Web browsers, navigating XSLT tools, transforming XML, and putting the technology to work. This book also covers the enhanced features of the XSLT, version 1.1.Table of ContentsIntroduction. Part I: Getting Started with XSLT. Chapter 1: Introducing the X-Team. Chapter 2: Writing Your First XSLT Stylesheet. Part II: Becoming an XSLT Transformer. Chapter 3: Transforming with Style (Stylesheets, That Is). Chapter 4: Templates Rule! Chapter 5: XPath Espresso. Chapter 6: We Want Results! Part III: Prime Time XSLT. Chapter 7: Adding Programming Logic Isn't Just for Propheads. Chapter 8: Variables in XSLT: A Breed Apart. Chapter 9: Tweaking the Results to Get What You Want. Chapter 10: To HTML and Beyond! Chapter 11: XPath Data Types and Functions. Part IV: eXtreme XSLT. Chapter 12: Combining XSLT Stylesheets. Chapter 13: "Gimme Some Space" and Other Output Issues. Chapter 14: Keys and Cross-Referencing. Chapter 15: Namespaces Revisited. Chapter 16: Extending XSLT. Chapter 17: Debugging XSLT Transformations. Part V: The Part of Tens. Chapter 18: Ten Most Confusing Things About XSLT. Chapter 19: Ten All-Pro XSLT Resources on the Web. Chapter 20: Ten XSLT Processors Available Online. Index. Book Registration Information.

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Book SynopsisAlthough JavaScript has become the programming language of the Web, it's a little different from the expectations of other languages. This convenient pocket reference gives you immediate answers to pressing questions as you encounter them.

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Book SynopsisTo guide you through this broad ecosystem, this popular guide provides essential concepts, methods, and techniques for digital design that have withstood the test of time. UX designers, product managers, developers, and anyone involved in digital design will learn how to create semantic structures that will help people engage with your message.

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Book SynopsisWith this practical guide, you'll learn how PHP has become a full-featured, mature language with object-orientation, namespaces, and a growing collection of reusable component libraries. You'll learn best practices for application architecture and planning, databases, security, testing, debugging, and deployment.

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Book SynopsisDon't accept the compromise between fast and beautiful: you can have it all. Phoenix creator Chris McCord, Elixir creator Jose Valim, and award-winning author Bruce Tate walk you through building an application that's fast and reliable. At every step, you'll learn from the Phoenix creators not just what to do, but why. Packed with insider insights and completely updated for Phoenix 1.4, this definitive guide will be your constant companion in your journey from Phoenix novice to expert, as you build the next generation of web applications. Phoenix is the long-awaited web framework based on Elixir, the highly concurrent language that combines a beautiful syntax with rich metaprogramming. The best way to learn Phoenix is to code, and you'll get to attack some interesting problems. Start working with controllers, views, and templates within the first few pages. Build an in-memory context, and then back it with an Ecto database layer, complete with changesets and constraints that keep readers informed and your database integrity intact. Craft your own interactive application based on the channels API for the real-time applications that this ecosystem made famous. Write your own authentication plugs, and use the OTP layer for supervised services. Organize code with modular umbrella projects. This edition is fully updated for Phoenix 1.4, with a new chapter on using Channel Presence to find out who's connected, even on a distributed application. Use the new generators and the new ExUnit features to organize tests and make Ecto tests concurrent. This is a book by developers and for developers, and we know how to help you ramp up quickly. Any book can tell you what to do. When you've finished this one, you'll also know why to do it. What You Need: To work through this book, you will need a computer capable of running Erlang 18 or higher, Elixir 1.5 or higher, and Phoenix 1.4 or higher. A rudimentary knowledge of Elixir is also highly recommended.

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Book SynopsisElixir and Phoenix are generating tremendous excitement as an unbeatable platform for building modern web applications. Make the most of them as you build a stateful web app with Elixir and OTP. Model domain entities without an ORM or a database. Manage server state and keep your code clean with OTP Behaviours. Layer on a Phoenix web interface without coupling it to the business logic. Open doors to powerful new techniques that will get you thinking about web development in fundamentally new ways. Elixir and OTP give us exceptional tools to build stateful back-end applications that really scale, with rock-solid reliability. In this book, you'll build a web application in ways that are radically different from the norm. The back end will be stateful, not stateless. Use persistent connections with Phoenix Channels instead of HTTP's request-response, and create the full application in distinct, decoupled layers. In Part 1, start by building the business logic as a separate application, without Phoenix. Model the application domain with Elixir Agents and simple data structures. By keeping state in memory instead of a database, you can reduce latency and simplify your code. Then add OTP Behaviours such as gen_server and gen_fsm that make managing in-memory state a breeze. Create a supervision tree to boost fault tolerance while separating error handling from business logic. Phoenix is a modern web framework you can layer on top of business logic while keeping the two completely decoupled. In Part 2, you'll do exactly that as you build a web interface with Phoenix. Bring in the application from Part 1 as a dependency to a new Phoenix project. Then use ultra-scalable Phoenix Channels to establish persistent connections between the stateful server and a stateful front-end client. You're going to love this way of building web apps! What You Need: You'll need a computer that can run Elixir version 1.3 or higher and Phoenix 1.2 or higher. Some familiarity with Elixir and Phoenix is recommended.

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Book SynopsisLanguages may come and go, but the relational database endures. Learn how to use Ecto, the premier database library for Elixir, to connect your Elixir and Phoenix apps to databases of the SQL and NoSQL variety. Get a firm handle on Ecto fundamentals with a module-by-module tour of the critical parts of Ecto. Then move on to more advanced topics and advice on best practices with a series of recipes that provide clear, step-by-step instructions on scenarios commonly encountered by app developers. Co-authored by the creator of Ecto, this title provides all the essentials you need to use Ecto effectively. Elixir and Phoenix are taking the application development world by storm, and Ecto, the database library that ships with Phoenix, is going right along with them. There are plenty of examples that show you the basics, but to use Ecto to its full potential, you need to learn the library from the ground up. This definitive guide starts with a tour of the core features of Ecto - repos, queries, schemas, changesets, transactions - gradually building your knowledge with tasks of ever-increasing complexity. Along the way, you'll be learning by doing - a sample application handles all the boilerplate so you can focus on getting Ecto into your fingers. Build on that core knowledge with a series of recipes featuring more advanced topics. Change your pooling strategy to maximize your database's efficiency. Use nested associations to handle complex table relationships. Add streams to handle large result sets with ease. Based on questions from Ecto users, these recipes cover the most common situations developers run into. Whether you're new to Ecto, or already have an app in production, this title will give you a deeper understanding of how Ecto works, and help make your database code cleaner and more efficient. What You Need: To follow along with the book, you should have Erlang/OTP 19+ and Elixir 1.4+ installed. The book will guide you through setting up a sample application that integrates Ecto.

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Book SynopsisDescription Phoenix is a modern web framework built for the Elixir programming language. Elegant, fault-tolerant, and performant, Phoenix is as easy to use as Rails and as rock-solid as Elixir’s Erlang-based foundation. Phoenix in Action builds on your existing web dev skills, teaching you the unique benefits of Phoenix along with just enough Elixir to get the job done. Phoenix in Action is an example-based tutorial that teaches you how to use the Phoenix framework to build production-quality web apps. Following a running example of an online auction site, you’ll design and build everything from the core components that drive the app to the real-time user interactions where Phoenix really shines. You’ll handle business logic, database interactions, and app designs that take advantage of functional programming as you discover a better way to develop web applications. Key features · Use channels for real-time communication · Learn database interactions with Ecto · Hands-on examples · Step-by-step instructions · Jargon-free Audience Written for web developers familiar with a framework like Rails or ASP.NET. No experience of Elixir or Phoenix required. About the technology Phoenix is a web framework for the Elixir language. Phoenix applications are blazingly fast, and as a developer you’ll appreciate the attention to detail in the framework design that makes you superproductive almost immediately. In particular, Phoenix channels provide an easy way to set up and manage real-time communication.

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Book SynopsisThe Hacker's Guide to Python will teach you how to fine tune your Python code and give you a deeper understanding of how the language works under the hood. This essential guide distils years of Python experience into a handy collection of general advice and specific tips that will help you pick the right libraries, distribute your code correctly, build future-proof programs, and optimise your programs down to the bytecode.Trade Review"Serious Python contains a considerable amount of judicious battle-tested advice from an experienced developer—as well as some insightful gems from the guest contributors—making the overall effort a welcome addition to the limited number of books aimed at more advanced Python programmers."—Michael J. Ross, web developer and former Slashdot contributorTable of ContentsIntroductionChapter 1: Starting Your ProjectChapter 2: Modules, Libraries, and FrameworksChapter 3: Documentation and Good API PracticeChapter 4: Handling Timestamps and Time ZonesChapter 5: Distributing Your SoftwareChapter 6: Unit TestingChapter 7: Methods and DecoratorsChapter 8: Functional ProgrammingChapter 9: The Abstract Syntax Tree, Hy, and Lisp-like AttributesChapter 10: Performances and OptimizationsChapter 11: Scaling and ArchitectureChapter 12: Managing Relational DatabasesChapter 13: Write Less, Code MoreIndex

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Book Synopsis Paul Deitel, CEO and Chief Technical Officer of Deitel & Associates, Inc., is a graduate of MIT, where he studied Information Technology. Through Deitel & Associates, Inc., he has delivered hundreds of programming courses worldwide to clients, including Cisco, IBM, Siemens, Sun Microsystems, Dell, Fidelity, NASA at the Kennedy Space Center, the National Severe Storm Laboratory, White Sands Missile Range, Rogue Wave Software, Boeing, SunGard Higher Education, Nortel Networks, Puma, iRobot, Invensys and many more. He and his co-author, Dr. Harvey M. Deitel, are the world's best-selling programming-language textbook/professional book/video authors. Dr. Harvey Deitel, Chairman and Chief Strategy Officer of Deitel & Associates, Inc., has over 50 years of experience in the computer field. Dr. Deitel earned B.S. and M.S. degrees in Electrical Engineering from MIT and a Ph.D. in Mathematics from Boston University. He has extensive college teaching experienTrade Review“The chapters are clearly written with detailed explanations of the example code. The modular structure, wide range of contemporary data science topics, and code in companion Jupyter notebooks make this a fantastic resource for readers of a variety of backgrounds. Fabulous Big Data chapter—it covers all of the relevant programs and platforms. Great Watson chapter! The chapter provides a great overview of the Watson applications. Also, your translation examples are great because they provide an ‘instant reward’—it’s very satisfying to implement a task and receive results so quickly. Machine Learning is a huge topic, and the chapter serves as a great introduction. I loved the California housing data example—very relevant for business analytics. The chapter was visually stunning.” —Alison Sanchez, Assistant Professor in Economics, University of San Diego “A great introduction to Big Data concepts, notably Hadoop, Spark, and IoT. The examples are extremely realistic and practical. The authors do an excellent job of combining programming and data science topics. The material is presented in digestible sections accompanied by engaging interactive examples. Nearly all concepts are accompanied by a worked-out example. A comprehensive overview of object-oriented programming in Python—the use of card image graphics is sure to engage the reader.” —Garrett Dancik, Eastern Connecticut State University “Covers some of the most modern Python syntax approaches and introduces community standards for style and documentation. The machine learning chapter does a great job of walking people through the boilerplate code needed for ML in Python. The case studies accomplish this really well. The later examples are so visual. Many of the model evaluation tasks make for really good programming practice. I can see readers feeling really excited about playing with the animations.” —Elizabeth Wickes, Lecturer, School of Information Sciences, University of Illinois at Urbana-Champaign “An engaging, highly accessible book that will foster curiosity and motivate beginning data scientists to develop essential foundations in Python programming, statistics, data manipulation, working with APIs, data visualization, machine learning, cloud computing, and more. Great walkthrough of the Twitter APIs—sentiment analysis piece is very useful. I’ve taken several classes that cover natural language processing and this is the first time the tools and concepts have been explained so clearly. I appreciate the discussion of serialization with JSON and pickling and when to use one or the other—with an emphasis on using JSON over pickle—good to know there’s a better, safer way!” —Jamie Whitacre, Data Science Consultant “For a while, I have been looking for a book in Data Science using Python that would cover the most relevant technologies. Well, my search is over. A must-have book for any practitioner of this field. The machine learning chapter is a real winner!! The dynamic visualization is fantastic.” —Ramon Mata-Toledo, Professor, James Madison University “I like the new combination of topics from computer science, data science, and stats. This is important for building data science programs that are more than just cobbling together math and computer science courses. A book like this may help facilitate expanding our offerings and using Python as a bridge for computer and data science topics. For a data science program that focuses on a single language (mostly), I think Python is probably the way to go.” —Lance Bryant, Shippensburg University “You’ll develop applications using industry standard libraries and cloud computing services.” —Daniel Chen, Data Scientist, Lander Analytics “Great introduction to Python! This book has my strongest recommendation both as an introduction to Python as well as Data Science.” —Shyamal Mitra, Senior Lecturer, University of Texas “IBM Watson is an exciting chapter. The code examples put together a lot of Watson services in a really nifty example.” —Daniel Chen, Data Scientist, Lander Analytics “Fun, engaging real-world examples will encourage readers to conduct meaningful data analyses. Provides many of the best explanations of data science concepts I’ve encountered. Introduces the most useful starter machine learning models—does a good job explaining how to choose the best model and what ‘the best’ means. Great overview of all the big data technologies with relevant examples.” —Jamie Whitacre, Data Science Consultant “A great introduction to deep learning.” —Alison Sanchez, University of San Diego “The best designed Intro to Data Science/Python book I have seen.” —Roland DePratti, Central Connecticut State University “I like the new combination of topics from computer science, data science, and stats.” —Lance Bryant, Shippensburg University “The book’s applied approach should engage readers. A fantastic job providing background on various machine learning concepts without burdening the users with too many mathematical details.” —Garrett Dancik, Assoc. Prof. of Computer Science/Bioinformatics, Eastern Connecticut State University “Helps readers leverage the large number of existing libraries to accomplish tasks with minimal code. Concepts are accompanied by rich Python examples that readers can adapt to implement their own solutions to data science problems. I like that cloud services are used.” —David Koop, Assistant Professor, U-Mass Dartmouth “I enjoyed the OOP chapter—doctest unit testing is nice because you can have the test in the actual docstring so things are traveling together. The line-by-line explanations of the static and dynamic visualizations of the die rolling example are just great.” —Daniel Chen, Data Scientist, Lander Analytics “A lucid exposition of the fundamentals of Python and Data Science. Thanks for pointing out seeding the random number generator for reproducibility. I like the use of dictionary and set comprehensions for succinct programming. ‘List vs. Array Performance: Introducing %timeit’ is convincing on why one should use ndarrays. Good defensive programming. Great section on Pandas Series and DataFrames—one of the clearest expositions that I have seen. The section on data wrangling is excellent. Natural Language Processing is an excellent chapter! I learned a tremendous amount going through it.” —Shyamal Mitra, Senior Lecturer, University of Texas “I like the discussion of exceptions and tracebacks. I really liked the Data Mining Twitter chapter; it focused on a real data source and brought in a lot of techniques for analysis (e.g., visualization, NLP). I like that the Python modules helped hide some of the complexity. Word clouds look cool.” —David Koop, Assistant Professor, U-Mass Dartmouth “I love the book! The examples are definitely a high point.” —Dr. Irene Bruno, George Mason University “I was very excited to see this book. I like its focus on data science and a general purpose language for writing useful data science programs. The data science portion distinguishes this book from most other introductory Python books.” —Dr. Harvey Siy, University of Nebraska at Omaha “I’ve learned a lot in this review process, discovering the exciting field of AI. I’ve liked the Deep Learning chapter, which has left me amazed with the things that have already been achieved in this field.” —José Antonio González Seco, Consultant “An impressive hands-on approach to programming meant for exploration and experimentation.” —Elizabeth Wickes, Lecturer, School of Information Sciences, University of Illinois at Urbana-Champaign “I was impressed at how easy it was to get started with NLP using Python. A meaningful overview of deep learning concepts, using Keras. I like the streaming example.” —David Koop, Assistant Professor, U-Mass Dartmouth “Really like the use of f-strings, instead of the older string-formatting methods. Seeing how easy TextBlob is compared to base NLTK was great. I never made word clouds with shapes before, but I can see this being a motivating example for people getting started with NLP. I’m enjoying the case-study chapters in the latter parts of the book. They are really practical. I really enjoyed working through all the Big Data examples, especially the IoT ones.” —Daniel Chen, Data Scientist, Lander Analytics “I really liked the live IPython input-output. The thing that I like most about this product is that it is a Deitel & Deitel book (I’m a big fan) that covers Python.” —Dr. Mark Pauley, University of Nebraska at Omaha Table of ContentsPreface xviiBefore You Begin xxxiiiChapter 1: Introduction to Computers and Python 11.1 Introduction 21.2 A Quick Review of Object Technology Basics 31.3 Python 51.4 It’s the Libraries! 71.5 Test-Drives: Using IPython and Jupyter Notebooks 91.6 The Cloud and the Internet of Things 161.7 How Big Is Big Data? 171.8 Case Study—A Big-Data Mobile Application 241.9 Intro to Data Science: Artificial Intelligence—at the Intersection of CS and Data Science 261.10 Wrap-Up 29Chapter 2: Introduction to Python Programming 312.1 Introduction 322.2 Variables and Assignment Statements 322.3 Arithmetic 332.4 Function print and an Intro to Single- and Double-Quoted Strings 362.5 Triple-Quoted Strings 382.6 Getting Input from the User 392.7 Decision Making: The if Statement and Comparison Operators 412.8 Objects and Dynamic Typing 452.9 Intro to Data Science: Basic Descriptive Statistics 462.10 Wrap-Up 48Chapter 3: Control Statements 493.1 Introduction 503.2 Control Statements 503.3 if Statement 513.4 if...else and if...elif...else Statements 523.5 while Statement 553.6 for Statement 553.7 Augmented Assignments 573.8 Sequence-Controlled Iteration; Formatted Strings 583.9 Sentinel-Controlled Iteration 593.10 Built-In Function range: A Deeper Look 603.11 Using Type Decimal for Monetary Amounts 613.12 break and continue Statements 643.13 Boolean Operators and, or and not 653.14 Intro to Data Science: Measures of Central Tendency—Mean, Median and Mode 673.15 Wrap-Up 69Chapter 4: Functions 714.1 Introduction 724.2 Defining Functions 724.3 Functions with Multiple Parameters 754.4 Random-Number Generation 764.5 Case Study: A Game of Chance 784.6 Python Standard Library 814.7 math Module Functions 824.8 Using IPython Tab Completion for Discovery 834.9 Default Parameter Values 854.10 Keyword Arguments 854.11 Arbitrary Argument Lists 864.12 Methods: Functions That Belong to Objects 874.13 Scope Rules 874.14 import: A Deeper Look 894.15 Passing Arguments to Functions: A Deeper Look 904.16 Recursion 934.17 Functional-Style Programming 954.18 Intro to Data Science: Measures of Dispersion 974.19 Wrap-Up 98Chapter 5: Sequences: Lists and Tuples 1015.1 Introduction 1025.2 Lists 1025.3 Tuples 1065.4 Unpacking Sequences 1085.5 Sequence Slicing 1105.6 del Statement 1125.7 Passing Lists to Functions 1135.8 Sorting Lists 1155.9 Searching Sequences 1165.10 Other List Methods 1175.11 Simulating Stacks with Lists 1195.12 List Comprehensions 1205.13 Generator Expressions 1215.14 Filter, Map and Reduce 1225.15 Other Sequence Processing Functions 1245.16 Two-Dimensional Lists 1265.17 Intro to Data Science: Simulation and Static Visualizations 1285.18 Wrap-Up 135Chapter 6: Dictionaries and Sets 1376.1 Introduction 1386.2 Dictionaries 1386.3 Sets 1476.4 Intro to Data Science: Dynamic Visualizations 1526.5 Wrap-Up 158Chapter 7: Array-Oriented Programming with NumPy 1597.1 Introduction 1607.2 Creating arrays from Existing Data 1607.3 array Attributes 1617.4 Filling arrays with Specific Values 1637.5 Creating arrays from Ranges 1647.6 List vs. array Performance: Introducing %timeit 1657.7 array Operators 1677.8 NumPy Calculation Methods 1697.9 Universal Functions 1707.10 Indexing and Slicing 1717.11 Views: Shallow Copies 1737.12 Deep Copies 1747.13 Reshaping and Transposing 1757.14 Intro to Data Science: pandas Series and DataFrames 1777.15 Wrap-Up 189Chapter 8: Strings: A Deeper Look 1918.1 Introduction 1928.2 Formatting Strings 1938.3 Concatenating and Repeating Strings 1968.4 Stripping Whitespace from Strings 1978.5 Changing Character Case 1978.6 Comparison Operators for Strings 1988.7 Searching for Substrings 1988.8 Replacing Substrings 1998.9 Splitting and Joining Strings 2008.10 Characters and Character-Testing Methods 2028.11 Raw Strings 2038.12 Introduction to Regular Expressions 2038.13 Intro to Data Science: Pandas, Regular Expressions and Data Munging 2108.14 Wrap-Up 214Chapter 9: Files and Exceptions 2179.1 Introduction 2189.2 Files 2199.3 Text-File Processing 2199.4 Updating Text Files 2229.5 Serialization with JSON 2239.6 Focus on Security: pickle Serialization and Deserialization 2269.7 Additional Notes Regarding Files 2269.8 Handling Exceptions 2279.9 finally Clause 2319.10 Explicitly Raising an Exception 2339.11 (Optional) Stack Unwinding and Tracebacks 2339.12 Intro to Data Science: Working with CSV Files 2359.13 Wrap-Up 241Chapter 10: Object-Oriented Programming 24310.1 Introduction 24410.2 Custom Class Account 24610.3 Controlling Access to Attributes 24910.4 Properties for Data Access 25010.5 Simulating “Private” Attributes 25610.6 Case Study: Card Shuffling and Dealing Simulation 25810.7 Inheritance: Base Classes and Subclasses 26610.8 Building an Inheritance Hierarchy; Introducing Polymorphism 26710.9 Duck Typing and Polymorphism 27510.10 Operator Overloading 27610.11 Exception Class Hierarchy and Custom Exceptions 27910.12 Named Tuples 28010.13 A Brief Intro to Python 3.7’s New Data Classes 28110.14 Unit Testing with Docstrings and doctest 28710.15 Namespaces and Scopes 29010.16 Intro to Data Science: Time Series and Simple Linear Regression 29310.17 Wrap-Up 301Chapter 11: Natural Language Processing (NLP) 30311.1 Introduction 30411.2 TextBlob 30511.3 Visualizing Word Frequencies with Bar Charts and Word Clouds 31911.4 Readability Assessment with Textatistic 32411.5 Named Entity Recognition with spaCy 32611.6 Similarity Detection with spaCy 32711.7 Other NLP Libraries and Tools 32811.8 Machine Learning and Deep Learning Natural Language Applications 32811.9 Natural Language Datasets 32911.10 Wrap-Up 330Chapter 12: Data Mining Twitter 33112.1 Introduction 33212.2 Overview of the Twitter APIs 33412.3 Creating a Twitter Account 33512.4 Getting Twitter Credentials—Creating an App 33512.5 What’s in a Tweet? 33712.6 Tweepy 34012.7 Authenticating with Twitter Via Tweepy 34112.8 Getting Information About a Twitter Account 34212.9 Introduction to Tweepy Cursors: Getting an Account’s Followers and Friends 34412.10 Searching Recent Tweets 34712.11 Spotting Trends: Twitter Trends API 34912.12 Cleaning/Preprocessing Tweets for Analysis 35312.13 Twitter Streaming API 35412.14 Tweet Sentiment Analysis 35912.15 Geocoding and Mapping 36212.16 Ways to Store Tweets 37012.17 Twitter and Time Series 37012.18 Wrap-Up 371Chapter 13: IBM Watson and Cognitive Computing 37313.1 Introduction: IBM Watson and Cognitive Computing 37413.2 IBM Cloud Account and Cloud Console 37513.3 Watson Services 37613.4 Additional Services and Tools 37913.5 Watson Developer Cloud Python SDK 38113.6 Case Study: Traveler’s Companion Translation App 38113.7 Watson Resources 39413.8 Wrap-Up 395Chapter 14: Machine Learning: Classification, Regression and Clustering 39714.1 Introduction to Machine Learning 39814.2 Case Study: Classification with k-Nearest Neighbors and the Digits Dataset, Part 1 40314.3 Case Study: Classification with k-Nearest Neighbors and the Digits Dataset, Part 2 41314.4 Case Study: Time Series and Simple Linear Regression 42014.5 Case Study: Multiple Linear Regression with the California Housing Dataset 42514.6 Case Study: Unsupervised Machine Learning, Part 1—Dimensionality Reduction 43814.7 Case Study: Unsupervised Machine Learning, Part 2—k-Means Clustering 44214.8 Wrap-Up 455Chapter 15: Deep Learning 45715.1 Introduction 45815.2 Keras Built-In Datasets 46115.3 Custom Anaconda Environments 46215.4 Neural Networks 46315.5 Tensors 46515.6 Convolutional Neural Networks for Vision; Multi-Classification with the MNIST Dataset 46715.7 Visualizing Neural Network Training with TensorBoard 48615.8 ConvnetJS: Browser-Based Deep-Learning Training and Visualization 48915.9 Recurrent Neural Networks for Sequences; Sentiment Analysis with the IMDb Dataset 48915.10 Tuning Deep Learning Models 49715.11 Convnet Models Pretrained on ImageNet 49815.12 Wrap-Up 499Chapter 16: Big Data: Hadoop, Spark, NoSQL and IoT 50116.1 Introduction 50216.2 Relational Databases and Structured Query Language (SQL) 50616.3 NoSQL and NewSQL Big-Data Databases: A Brief Tour 51716.4 Case Study: A MongoDB JSON Document Database 52016.5 Hadoop 53016.6 Spark 54116.7 Spark Streaming: Counting Twitter Hashtags Using the pyspark-notebook Docker Stack 55116.8 Internet of Things and Dashboards 56016.9 Wrap-Up 571Index 573

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