Chapter 8. Debugging h2; Web Browser Developer Tools; Chrome Developer Tools; Firefox Developer Tools; Debugging h2 on iOS Using Charles Proxy; Debugging h2 on Android; WebPagetest; OpenSSL; OpenSSL Commands; nghttp2; Using nghttp; curl; Using curl; h2i; Wireshark; Summary; Chapter 9. What Is Next?; TCP or UDP?; QUIC; TLS 1.3; HTTP/3?; Summary; Appendix A. HTTP/2 Frames; The Frame Header; DATA; DATA Frame Fields; DATA Frame Flags; HEADERS; HEADERS Frame Fields; HEADERS Frame Flags; PRIORITY; PRIORITY Frame Fields; RST_STREAM; SETTINGS; SETTINGS Parameters; PUSH_PROMISE.

Summary HTTP/2 in Action is a complete guide to HTTP/2, one of the core protocols of the web. Because HTTP/2 has been designed to be easy to transition to, including keeping it backwards compatible, adoption is rapid and expected to increase over the next few years. Concentrating on practical matters, this interesting book presents key HTTP/2 concepts such as frames, streams, and multiplexing and explores how they affect the performance and behavior of your websites. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology HTTP—Hypertext Transfer Protocol—is the standard for exchanging messages between websites and browsers. And after 20 years, it's gotten a much-needed upgrade. With support for streams, server push, header compression, and prioritization, HTTP/2 delivers vast improvements in speed, security, and efficiency. About the Book HTTP/2 in Action teaches you everything you need to know to use HTTP/2 effectively. You'll learn how to optimize web performance with new features like frames, multiplexing, and push. You'll also explore real-world examples on advanced topics like flow control and dependencies. With ready-to-implement tips and best practices, this practical guide is sure to get you—and your websites—up to speed! What's Inside HTTP/2 for web developers Upgrading and troubleshooting Real-world examples and case studies QUIC and HTTP/3 About the Reader Written for web developers and site administrators. About the Authors Barry Pollard is a professional developer with two decades of experience developing, supporting, and tuning software and infrastructure. Table of Contents PART 1 MOVING TO HTTP/2 Web technologies and HTTP The road to HTTP/2 Upgrading to HTTP/2 PART 2 USING HTTP/2 HTTP/2 protocol basics Implementing HTTP/2 push Optimizing for HTTP/2 PART 3 ADVANCED HTTP/2 Advanced HTTP/2 concepts HPACK header compression PART 4 THE FUTURE OF HTTP TCP, QUIC, and HTTP/3 Where HTTP goes from here

What can your organization gain by adopting HTTP/2? How about faster, simpler, and more robust websites and applications? This practical guide demonstrates how the latest version of the Hypertext Transfer Protocol can dramatically improve website and application performance. You’ll take a deep dive into HTTP/2 details, and learn how this updated protocol is changing the web landscape. HTTP/1.1 has been the primary means of communicating data across the web for the past 20 years, but the level of interaction today has gone well beyond what people envisioned in 1997. With this book, authors Stephen Ludin and Javier Garza show you how HTTP/2 will help speed the execution of modern sites and applications. With this book, you’ll explore: Performance challenges that led to the HTTP upgrade HTTP/2 in a nutshell, including benefits and transition methods Existing best practices and hacks to improve web performance HTTP/2 support for browsers, servers, proxies, and content delivery networks How the performance of sites using HTTP/2 compares to their HTTP/1.1 experience HTTP/2’s effect on specific issues such as latency, packet loss, and Time to First Byte (TTFB) HTTP/2’s effect on specific issues such as latency, packet loss, and Time to First Byte (TTFB)

An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

This guide gives a complete and detailed description of the HTTP protocol and how it shapes the landscape of the Web by the technologies that it supports.

Like the first edition, the second edition of Learning by Doing: A Handbook for Professional Learning Communities at Work helps educators close the knowing-doing gap as they transform their schools into professional learning communities (PLCs).

The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.