Böcker utgivna av Manning Publications

Deep learning offers the potential to identify complex patterns and relationships hidden in data of all sorts. Deep Learning with Structured Data shows you how to apply powerful deep learning analysis techniques to the kind of structured, tabular data you'll find in the relational databases that real-world businesses depend on. Filled with practical, relevant applications, this book teaches you how deep learning can augment your existing machine learning and business intelligence systems.

The Microsoft Azure cloud is an ideal platform for data-intensive applications. Designed for productivity, Azure provides pre-built services that make collection, storage, and analysis much easier to implement and manage. Azure Data Engineering teaches you how to design a reliable, performant, and cost-effective data infrastructure in Azure by progressively building a complete working analytics system.

Computer vision is central to many leading-edge innovations, including self-driving cars, drones, augmented reality, facial recognition, and much, much more. Amazing new computer vision applications are developed every day, thanks to rapid advances in AI and deep learning (DL). Deep Learning for Vision Systems teaches you the concepts and tools for building intelligent, scalable computer vision systems that can identify and react to objects in images, videos, and real life. With author Mohamed Elgendy's expert instruction and illustration of real-world projects, you'll finally grok state-of-the-art deep learning techniques, so you can build, contribute to, and lead in the exciting realm of computer vision! Key Features· Introduction to computer vision· Deep learning and neural network· Transfer learning and advanced CNN architectures· Image classification and captioning For readers with intermediate Python, math and machine learningskills. About the technology By using deep neural networks, AI systems make decisions based on their perceptions of their input data. Deep learning-based computer vision (CV) techniques, which enhance and interpret visual perceptions, makes tasks like image recognition, generation, and classification possible. Mohamed Elgendy is the head of engineering at Synapse Technology, a leading AI company that builds proprietary computer vision applications to detect threats at security checkpoints worldwide. Previously, Mohamed was an engineering manager at Amazon, where he developed and taught the deep learning for computer vision course at Amazon's Machine Learning University. He also built and managed Amazon's computer vision think tank, among many other noteworthy machine learning accomplishments. Mohamed regularly speaks at many AI conferences like Amazon's DevCon, O'Reilly's AI conference and Google's I/O.

We all learn through trial and error. We avoid the things that cause us to experience pain and failure. We embrace and build on the things that give us reward and success. This common pattern is the foundation of deep reinforcement learning: building machine learning systems that explore and learn based on the responses of the environment. Grokking Deep Reinforcement Learning introduces this powerful machine learning approach, using examples, illustrations, exercises, and crystal-clear teaching. You'll love the perfectly paced teaching and the clever, engaging writing style as you dig into this awesome exploration of reinforcement learning fundamentals, effective deep learning techniques, and practical applications in this emerging field.

Turn the corner from ?Haskell student? to ?Haskell developer.? Haskell in Depth explores the important language features and programming skills you'll need to build production-quality software using Haskell. And along the way, you'll pick up some interesting insights into why Haskell looks and works the way it does. Get ready to go deep! Haskell in Depth is the perfect second book on Haskell. After a quick refresher on Haskell basics, this hands-on guide dives into examples and application scenarios designed to teach how Haskell works and how to apply it correctly. You'll learn about managing projects with Cabal and Stack, tackle error-handling and testing, and package programs and libraries for production deployment. Key Features· Organizing your projects with Cabal and Stack· Testing and profiling· Working with data· Building web services and networking apps· Using the sophisticated libraries like lens, vinyl, and servant Written for developers familiar with Haskell basics. About the technology As software becomes more complex, it's essential to program efficiently using tools and techniques that guarantee your applications will run correctly, grow easily, and last a long time. Haskell is a functional programming language that blends a mathematically-rigorous approach to software design with a tested ecosystem of tools and libraries you can use to build deployable applications.

Unit Testing: Principles, Patterns and Practices shows you how to refine your existing unit tests by implementing modern best practices. YouGÇÖll learn to spot which tests are performing, which need refactoring, and which need to be deleted entirely! Upgrade your testing suite with new testing styles, good patterns, and reliable automated testing. Unit Testing: Principles, Practices and Patterns is a practical guide to modern unit testing best practices. Microsoft MVP Vladimir Khorikov takes you hands-on with examples of the ideal unit test and unit testing practices, building your skills step by step on a solid foundation. Key Features -+ A universal frame of reference by which to assess any unit test -+ Common anti-patterns to identify and avoid -+ Guidelines on how to refactor a test suite along with the -+ production code it covers -+ Using integration tests to verify the behavior of the system as a whole For readers who know the basics of unit testing and want to improve their techniques to get the best return on their efforts. Examples are in C# but apply in any language About the technology Test automation has turned unit testing into a standard practice in software development. The right unit test suite gives great outcomes and is vital to delivering quality software. Author Bio Vladimir Khorikov is an author, blogger, and Microsoft MVP. He has been developing software professionally for over ten years, and has mentored numerous teams on the ins and outs of unit testing.

The Spark distributed data processing platform provides an easy-to-implement tool for ingesting, streaming, and processing data from any source. In Spark in Action, Second Edition, you'll learn to take advantage of Spark's core features and incredible processing speed, with applications including real-time computation, delayed evaluation, and machine learning. Unlike many Spark books written for data scientists, Spark in Action, Second Edition is designed for data engineers and software engineers who want to master data processing using Spark without having to learn a complex new ecosystem of languages and tools. You'll instead learn to apply your existing Java and SQL skills to take on practical, real-world challenges. Key Features· Lots of examples based in the Spark Java APIs using real-life dataset and scenarios · Examples based on Spark v2.3 Ingestion through files, databases, and streaming · Building custom ingestion process · Querying distributed datasets with Spark SQL For beginning to intermediate developers and data engineers comfortable programming in Java. No experience with functional programming, Scala, Spark, Hadoop, or big data is required. About the technology Spark is a powerful general-purpose analytics engine that can handle massive amounts of data distributed across clusters with thousands of servers. Optimized to run in memory, this impressive framework can process data up to 100x faster than most Hadoop-based systems. Author BioAn experienced consultant and entrepreneur passionate about all things data, Jean-Georges Perrin was the first IBM Champion in France, an honor he's now held for ten consecutive years. Jean-Georges has managed many teams of software and data engineers.

Hello World! Third Edition is a fun, easy-to-use guide with copious illustrations and engaging examples. It takes the reader on a playful tour of basic programming concepts and then puts those concepts together to make fun and useful programs. It uses Python, a programming language that is one of the easiest to read, write, and understand.Like the previous two editions, Hello World! Third Edition is not just for kids. While the tone is light and engaging, it doesn't "talk down" to the reader, and beginners of any age will love its readability and sense of humor. Written by Warren Sande and his son, Carter, it is full of examples that will get you thinking and learning. Reviewed by professional educators, this book is kid-tested and parent-approved. You don't need to know anything about programming to use the book, just the basics of using a computer. If you can start a program and save a file, you can learn to program using this book!Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

Object Design Style Guide captures dozens of techniques for creating pro-quality OO code that can stand the test of time. Examples are in an instantly-familiar pseudocode, teaching techniques you can apply to any OO language, from C++ to PHP. The design rules for different types of objectsBest practices for naming objectsTesting an object's behavior instead of its implementationExercises for each chapter to test your design skills

With an emphasis on clarity, style, and performance, author J.T. Wolohan expertly guides you through implementing a functionally-influenced approach to Python coding. You'll get familiar with Python's functional built-ins like the functools operator and itertools modules, as well as the tools library. Mastering Large Datasets teaches you to write easily readable, easily scalable Python code that can efficiently process large volumes of structured and unstructured data. By the end of this comprehensive guide, you'll have a solid grasp on the tools and methods that will take your code beyond the laptop and your data science career to the next level!Key features• An introduction to functional and parallel programming • Data science workflow • Profiling code for better performance • Fulfilling different quality objectives for a single unifying task • Python multiprocessing • Practical exercises including full-scale distributed applicationsAudienceReaders should have intermediate Python programming skills.About the technologyPython is a data scientist's dream-come-true, thanks to readily available libraries that support tasks like data analysis, machine learning, visualization, and numerical computing.

Practices of the Python Pro teaches you to design and write software that's easy to understand, maintain, and scale. Using practical Python-based examples, experienced developer Dane Hillard introduces design concepts that lay the foundation for designing, building, and maintaining software that's good for the long haul. You'll learn established guidelines for clean coding style and explore techniques to address design considerations like speed, integrity, security, and extensibility. Organizing large Python projectsLeveraging abstraction for clean codeWriting flexible, reusable codeInheritance and compositionConsiderations for testing and performance

Humans do a great job of reading text, identifying key ideas, summarizing, making connections, and other tasks that require comprehension and context. Recent advances in deep learning make it possible for computer systems to achieve similar results. Deep Learning for Natural Language Processing teaches you to apply deep learning methods to natural language processing (NLP) to interpret and use text effectively. In this insightful book, (NLP) expert Stephan Raaijmakers distills his extensive knowledge of the latest state-of-the-art developments in this rapidly emerging field. Key featuresAn overview of NLP and deep learning• Models for textual similarity• Deep memory-based NLP • Semantic role labeling • Sequential NLPAudienceFor those with intermediate Python skills and general knowledge of NLP. No hands-on experience with Keras or deep learning toolkits is required.About the technologyNatural language processing is the science of teaching computers to interpret and process human language. Recently, NLP technology has leapfrogged to exciting new levels with the application of deep learning, a form of neural network-based machine learningStephan Raaijmakers is a senior scientist at TNO and holds a PhD in machine learning and text analytics. He's the technical coordinator of two large European Union-funded research security-related projects. He's currently anticipating an endowed professorship in deep learning and NLP at a major Dutch university.

Type-related failures are common and can be very costly. Famously, in 1999, NASA's Mars Climate Orbiter burned up in the atmosphere because of an error that could have easily been prevented with typing. By taking advantage of the strong type systems available in most modern programming languages, you can eliminate whole classes of errors. Programming with Types teaches you type system techniques for writing software that's safe, correct, easy to test and maintain, and that practically documents itself. Master these techniques, and you may even help prevent an interstellar catastrophe!Key features• Designing functions that don't return values • An introduction to type systems and their benefits • Encoding text• Building data structures with arrays and references • Type composition • Function types • Type safety • Object-oriented programming with typesAudienceReaders need basic high school math skills as well as some programming experience in a mainstream programming language like Java, JavaScript, C#, or C++.About the technologyAs software grows in complexity, it's essential to adopt coding tools and strategies that enforce safety and correctness. A type system governs what types of data are allowable, requiring your code to accurately store, represent, and use its internal data.

Thanks to the authors' easy-to-digest style, you'll effortlessly learn about types, object-oriented programming with classes and interfaces, and using TypeScript with JavaScript libraries. You'll discover TypeScript's excellent tooling as you explore code-quality improvement with TSLint, debugging with source maps, unit testing, and more. TypeScript is JavaScript with an important upgrade! By adding a strong type system to JavaScript, TypeScript can help you eliminate entire categories of runtime errors. In TypeScript Quickly, you'll learn to build rock-solid apps through practical examples and hands-on projects under the expert instruction of experienced web developers Yakov Fain and Anton Moiseev. WILL SELL LIKE Angular Development with Typescript, Key features• Mastering TypeScript syntax • Object-oriented programming with classes and interfaces • Using TypeScript with JavaScript libraries • Multiple real-world code samplesAudienceWritten for intermediate web developers comfortable with JavaScript ES5 and HTML.About the technologyTypeScript is an extension of JavaScript that includes key language features such as optional static typing, compile-time error catching, and auto-complete. By specifying types and type annotations, your code becomes much easier to interpret, which improves productivity and team development. In particular, TypeScript makes complex applications like SPAs much easier to maintain and extend.Yakov Fain and Anton Moiseev are experienced web application developers. They authored two editions of Manning's Angular Development with TypeScript among other technical books. Yakov is a Java champion and a prolific tech blogger at yakov.fain.com.

Whether for building interactive browser-based applications or creating server-side applications in Node, JavaScript is the most widely used language for web programming. With new features, language improvements, paradigms, and potential use cases appearing regularly, there's never been a more exciting time to be a JavaScript developer. In The Joy of JavaScript, author and JavaScript expert Luis Atencio teaches you key design concepts that lead to clean, lean, modular, and easy-to-maintain code.Key features• JavaScript's objects and module system • Working with higher order functions • Driving application business logic with functional programming principles • Dynamically hooking into data with Proxy and Reflect APIs • Static type-checking with Flow • Taming complex asynchronous behavior using reactive programmingAudiencePerfect for intermediate JavaScript developers with basic familiarity with HTTP, HTML/CSS, and Git/CLI.About the technologyJavaScript is multi-paradigm, supporting object-oriented, functional, reactive, and event-driven styles of programming. And transpilers like Babel make it possible to compile code written in other languages into JavaScript.

?Atwood's Law? is driven by the idea that all applications will ultimately wind up on the web and therefore must be written in JavaScript. WebAssembly may be your way out! With WebAssembly, you can write in nearly any modern language and run your code in the browser through a memory-safe, sandboxed execution environment that can be embedded in a web browser and other platforms. Getting set up and moving with WebAssembly requires you to modify your web dev process; WebAssembly in Action will get you started quickly and guide you through real-world examples and detailed diagrams that help you create, run, and debug WebAssembly modules.Key features• The WebAssembly format • Dynamic linking of multiple modules at runtime • Communicating between modules and JavaScript • Debugging modules • Unreleased features like host bindings and garbage collectionAudienceWritten for developers with a basic understanding of C/C++, JavaScript, and HTML.About the technologyWebAssembly compiles code written in C, C++, Rust, and other highlevel languages into an ultra-compact bytecode files that run in the browser and other environments at near-native speeds. Besides vastly improved performance, opportunities to reuse existing code, and the freedom to build in your favorite language, this game-changer allows access to newer and faster libraries. WebAssembly is the joint effort of industry leaders like Microsoft, Mozilla, Google, and others, with

Humans learn best from feedback?we are encouraged to take actions that lead to positive results while deterred by decisions with negative consequences. This reinforcement process can be applied to computer programs allowing them to solve more complex problems that classical programming cannot. Deep Reinforcement Learning in Action teaches you the fundamental concepts and terminology of deep reinforcement learning, along with the practical skills and techniques you'll need to implement it into your own projects.Key features• Structuring problems as Markov Decision Processes • Popular algorithms such Deep Q-Networks, Policy Gradient method and Evolutionary Algorithms and the intuitions that drive them • Applying reinforcement learning algorithms to real-world problemsAudienceYou'll need intermediate Python skills and a basic understanding of deep learning.About the technologyDeep reinforcement learning is a form of machine learning in which AI agents learn optimal behavior from their own raw sensory input. The system perceives the environment, interprets the results of its past decisions, and uses this information to optimize its behavior for maximum long-term return. Deep reinforcement learning famously contributed to the success of AlphaGo but that's not all it can do!

If you think "Modern" and "C" don''t belong in the same sentence, think again. The C standards committee actively reviews and extends the language, with updated published C standards as recently as 2018. In Modern C, author Jens Gustedt teaches you the skills and features you need to write relevant programs in this tried-and-true language, including Linux and Windows, device drivers, web servers and browsers, smartphones, and much more! Modern C teaches you to take your C programming skills to new heights, whether you''re just starting out with C or have more extensive experience. Organized by level, this comprehensive guide lets you jump in where it suits you best while still reaping the maximum benefits. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

Summary Classic Computer Science Problems in Python deepens your knowledge of problem-solving techniques from the realm of computer science 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! Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Computer science problems that seem new or unique are often rooted in classic algorithms, coding techniques, and engineering principles. And classic approaches are still the best way to solve them! Understanding these techniques in Python expands your potential for success in web development, data munging, machine learning, and more. About the Book Classic Computer Science Problems in Python sharpens your CS problem-solving skills with time-tested scenarios, exercises, and algorithms, using Python. You'll tackle dozens of coding challenges, ranging from simple tasks like binary search algorithms to clustering data using k-means. You'll especially enjoy the feeling of satisfaction as you crack problems that connect computer science to the real-world concerns of apps, data, performance, and even nailing your next job interview! What's Inside Search algorithms Common techniques for graphs Neural networks Genetic algorithms Adversarial search Uses type hints throughout Covers Python 3.7   About the Reader For intermediate Python programmers. About the Author David Kopec is an assistant professor of Computer Science and Innovation at Champlain College in Burlington, Vermont. He is the author of Dart for Absolute Beginners (Apress, 2014) and Classic Computer Science Problems in Swift (Manning, 2018). Table of Contents Small problems Search problems Constraint-satisfaction problems Graph problems Genetic algorithms K-means clustering Fairly simple neural networks Adversarial search Miscellaneous problems    

SummaryDask is a native parallel analytics tool designed to integrate seamlessly with the libraries you''re already using, including Pandas, NumPy, and Scikit-Learn. With Dask you can crunch and work with huge datasets, using the tools you already have. And Data Science with Python and Dask is your guide to using Dask for your data projects without changing the way you work!Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. You''ll find registration instructions inside the print book.About the TechnologyAn efficient data pipeline means everything for the success of a data science project. Dask is a flexible library for parallel computing in Python that makes it easy to build intuitive workflows for ingesting and analyzing large, distributed datasets. Dask provides dynamic task scheduling and parallel collections that extend the functionality of NumPy, Pandas, and Scikit-learn, enabling users to scale their code from a single laptop to a cluster of hundreds of machines with ease.About the BookData Science with Python and Dask teaches you to build scalable projects that can handle massive datasets. After meeting the Dask framework, you''ll analyze data in the NYC Parking Ticket database and use DataFrames to streamline your process. Then, you''ll create machine learning models using Dask-ML, build interactive visualizations, and build clusters using AWS and Docker. What''s insideWorking with large, structured and unstructured datasetsVisualization with Seaborn and DatashaderImplementing your own algorithmsBuilding distributed apps with Dask DistributedPackaging and deploying Dask appsAbout the ReaderFor data scientists and developers with experience using Python and the PyData stack.About the AuthorJesse Daniel is an experienced Python developer. He taught Python for Data Science at the University of Denver and leads a team of data scientists at a Denver-based media technology company.Table of ContentsPART 1 - The Building Blocks of scalable computingWhy scalable computing matters Introducing Dask PART 2 - Working with Structured Data using Dask DataFrames Introducing Dask DataFrames Loading data into DataFrames Cleaning and transforming DataFrames Summarizing and analyzing DataFrames Visualizing DataFrames with Seaborn Visualizing location data with Datashader PART 3 - Extending and deploying DaskWorking with Bags and Arrays Machine learning with Dask-ML Scaling and deploying Dask

SummaryCloud Native Patternsis your guide to developing strong applications that thrive in the dynamic, distributed, virtual world of the cloud. This book presents a mental model for cloud-native applications, along with the patterns, practices, and tooling that set them apart.Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.About the TechnologyCloud platforms promise the holy grail: near-zero downtime, infinite scalability, short feedback cycles, fault-tolerance, and cost control. But how do you get there? By applying cloudnative designs, developers can build resilient, easily adaptable, web-scale distributed applications that handle massive user traffic and data loads. Learn these fundamental patterns and practices, and you''ll be ready to thrive in the dynamic, distributed, virtual world of the cloud.About the BookWith 25 years of experience under her belt, Cornelia Davis teaches you the practices and patterns that set cloud-native applications apart. With realistic examples and expert advice for working with apps, data, services, routing, and more, she shows you how to design and build software that functions beautifully on modern cloud platforms. As you read, you will start to appreciate that cloud-native computing is more about the how and why rather than the where. What''s insideThe lifecycle of cloud-native appsCloud-scale configuration managementZero downtime upgrades, versioned services, and parallel deploysService discovery and dynamic routingManaging interactions between services, including retries and circuit breakersAbout the ReaderRequires basic software design skills and an ability to read Java or a similar language.About the AuthorCornelia Davis is Vice President of Technology at Pivotal Software. A teacher at heart, she''s spent the last 25 years making good software and great software developers.Table of ContentsPART 1 - THE CLOUD-NATIVE CONTEXTYou keep using that word: Defining "cloud-native"Running cloud-native applications in productionThe platform for cloud-native softwarePART 2 - CLOUD-NATIVE PATTERNSEvent-driven microservices: It''s not just request/responseApp redundancy: Scale-out and statelessnessApplication configuration: Not just environment variablesThe application lifecycle: Accounting for constant changeAccessing apps: Services, routing, and service discoveryInteraction redundancy: Retries and other control loopsFronting services: Circuit breakers and API gatewaysTroubleshooting: Finding the needle in the haystackCloud-native data: Breaking the data monolith

Web APIs are everywhere, giving developers an efficient way to interact with applications, services, and data. Well-designed APIs are a joy to use; poorly-designed APIs are cumbersome, confusing, and frustrating. The Design of EverydayAPIs is a practical, example packed guide to crafting extraordinary web APIs. Author Arnaud Lauret demonstrates fantastic design principles and techniques you can apply to both public and private web APIs. The Design of Everyday APIs introduces you to the challenging and creative world of API design. In this experience-driven guide, you'll learn to think like an API designer, embracing effective practices for requirements gathering, blending business and technical goals, and adopting a consumer-first mindset. You'll master the lifecycle of web API design, including the all-important secure-by-design approach. This book teaches principles that can be applied in any style of API. Most examples are illustrated using REST and the OpenAPI specification. Written for developers with minimal experience building and consuming APIs. Key Features Characteristics of a well-designed API Explore the full API design lifecycle Designing user-oriented APIs Secure APIs by design Evolving existing APIs Validating your API designs Arnaud Lauret is a software architect with 15 years of experience in the banking industry. He has spent a decade using, designing, and building APIs. He's known on the web as the API Handyman (http://apihandyman.io) and is the creator the API Stylebook (http://apistylebook.com), a collection of resources for API designers.

Elm is more than just a cutting-edge programming language, it's a chance to upgrade the way you think about building web applications. Once you get comfortable with Elm's refreshingly different approach to application development, you'll be working with a clean syntax, dependable libraries, and a delightful compiler that essentially eliminates runtime exceptions. Elm compiles to JavaScript, so your code runs in any browser, and Elm's best-in-class rendering speed will knock your socks off. Let's get started! Elm in Action teaches you how to build well-designed, highly performant web applications using the Elm language. As you read, you'll follow an application called Photo Groove that will show you how to structure your application's domain and behaviour, how to maintain a pleasantly modular architecture, and how to deliver a quality product using the Elm language. Along the way, you'll learn how to interact smoothly with browser functionality, servers, and JavaScript libraries, as well and picking up a few tricks for building fluid, intuitive user experiences. If you're new to functional programming, you'll also gain a practical understanding of techniques that will make you a better developer no matter what you're writing! Written for readers comfortable with web application development in JavaScript. No experience with Elm or functional programming required. Key Features Getting started with the Elm language Scalable design for production web applications Single-page applications in Elm Testing your applications Richard Feldman is a programmer and entrepreneur who loves to push the limits of browser-based programming. He's a front-end engineer at NoRedInk and a well-known member of the Elm community.

With DApps built on the Ethereum blockchain, users can establish persistent, reliable ownership records for actions such as buying a car or a house with smart contracts to enforce blockchain-based verification before a transaction can be completed. Building Ethereum DApps introduces readers to decentralized applications based on the Ethereum blockchain platform. They'll discover how to design smart contracts to enforce transaction rules, and then implement them in Ethereum's JavaScript-like Solidity language. Key Features· Understanding decentralized applications and the underlying technologies· Learning the key components of the Ethereum platform· Implementing smart contracts in Solidity AudienceWritten for developers with intermediate experience in an OO language or in JavaScript. Some understanding and experience with blockchain concepts is helpful. Author BioRoberto Infante is an independent software development consultant who specializes in finance. Throughout his career, he has worked on a variety of innovative projects, such as the first internet mortgage approval system in the UK and one of the first online insurance underwriting systems in London.

Programmer's Guide to Apache Thrift provides comprehensive coverage of the Apache Thrift framework along with a developer's-eye view of modern distributed application architecture. Packed with complete code examples and pragmatic discussion, this book lays the best practices for multi-language distributed application development. You'll take a guided tour through transports, protocols, IDL and servers as you explore complete example programs in C++, Java and Python. You'll also learn how to work with platforms ranging from enterprise servers to mobile clients. About the technologyAny distributed application includes individual components, often written in different languages and hosted in multiple locations, which must communicate quickly and efficiently. Apache Thrift is a communication framework that enables cross-language remote procedure calls and serialization. Apache Thrift supports embedded, mobile, web, and server environments and a host of languages ranging from JavaScript to C++. It's perfect for back end services and embedded systems where size, scalability and performance are mission critical. Key Features Clear, concise coverage of all of the primary Apache Thrift features Complete coverage of the Apache Thrift Interface Definition Language Building and serializing complex user defined types Working with plug in serialization protocols and data compression Creating cross-language services Tools and features to enable interface evolutionRandy Abernethy is an active Apache Thrift contributor and can be found on the dev and user email lists. A serial entrepreneur, Randy founded Hollywood's first all hard disk recording studio in the early 90s, a direct market access institutional brokerage in the 2000s, and has recently focused on the development of proprietary automated trading systems using Apache Thrift for interoperability.

SummaryDeep Learning and the Game of Go teaches you how to apply the power of deep learning to complex reasoning tasks by building a Go-playing AI. After exposing you to the foundations of machine and deep learning, you''ll use Python to build a bot and then teach it the rules of the game.Foreword by Thore Graepel, DeepMindPurchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.About the TechnologyThe ancient strategy game of Go is an incredible case study for AI. In 2016, a deep learning-based system shocked the Go world by defeating a world champion. Shortly after that, the upgraded AlphaGo Zero crushed the original bot by using deep reinforcement learning to master the game. Now, you can learn those same deep learning techniques by building your own Go bot!About the BookDeep Learning and the Game of Go introduces deep learning by teaching you to build a Go-winning bot. As you progress, you''ll apply increasingly complex training techniques and strategies using the Python deep learning library Keras. You''ll enjoy watching your bot master the game of Go, and along the way, you''ll discover how to apply your new deep learning skills to a wide range of other scenarios!What''s insideBuild and teach a self-improving game AIEnhance classical game AI systems with deep learningImplement neural networks for deep learningAbout the ReaderAll you need are basic Python skills and high school-level math. No deep learning experience required.About the AuthorMax Pumperla and Kevin Ferguson are experienced deep learning specialists skilled in distributed systems and data science. Together, Max and Kevin built the open source bot BetaGo.Table of ContentsPART 1 - FOUNDATIONSToward deep learning: a machine-learning introductionGo as a machine-learning problemImplementing your first Go botPART 2 - MACHINE LEARNING AND GAME AIPlaying games with tree searchGetting started with neural networksDesigning a neural network for Go dataLearning from data: a deep-learning botDeploying bots in the wildLearning by practice: reinforcement learningReinforcement learning with policy gradientsReinforcement learning with value methodsReinforcement learning with actor-critic methodsPART 3 - GREATER THAN THE SUM OF ITS PARTSAlphaGo: Bringing it all togetherAlphaGo Zero: Integrating tree search with reinforcement learning

SummaryVoice Applications for Alexa and Google Assistant is your guide to designing, building, and implementing voice-based applications for Alexa and Google Assistant. Inside, you''ll learn how to build your own "skills"—the voice app term for actions the device can perform—from scratch.Foreword by Max Amordeluso.Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. You''ll find registration instructions inside the print book.About the TechnologyIn 2018, an estimated 100 million voice-controlled devices were installed in homes worldwide, and the apps that control them, like Amazon Alexa and Google Assistant, are getting more powerful, with new skills being added every day. Great voice apps improve how users interact with the web, whether they''re checking the weather, asking for sports scores, or playing a game.About the BookVoice Applications for Alexa and Google Assistant is your guide to designing, building, and implementing voice-based applications for Alexa and Google Assistant. You''ll learn to build applications that listen to users, store information, and rely on user context, as you create a voice-powered sleep tracker from scratch. With the basics mastered, you''ll dig deeper into multiuse conversational flow and other more-advanced concepts. Smaller projects along the way reinforce your new techniques and best practices. What''s insideBuilding a call-and-response skillDesigning a voice user interfaceUsing conversational contextGoing multimodalTips and best practicesAbout the ReaderPerfect for developers with intermediate JavaScript skills and basic Node.js skills. No previous experience with voice-first platforms is required.About the AuthorDustin A. Coates is a developer who focuses on voice and conversational applications. He''s currently the voice search lead at Algolia and is also a Google Developers Expert for Assistant as well as cohost of the VUX World podcast.Table of ContentsIntroduction to voice first Building a call-and-response skill on Alexa Designing a voice user interface Using entity resolution and built?in intents in Alexa skills Making a conversational Alexa skill VUI and conversation best practices Using conversation tools to add meaning and usabilityDirecting conversation flowBuilding for Google Assistant Going multimodal Push interactions Building for actions on Google with the Actions SDK