Sega, on the other hand, was just a faltering arcade company with big aspirations and even bigger personalities. But that would all change with the arrival of Tom Kalinske, a man who knew nothing about videogames and everything about fighting uphill battles. His unconventional tactics, combined with the blood, sweat and bold ideas of his renegade employees, transformed Sega and eventually led to a ruthless David-and-Goliath showdown with rival Nintendo.The battle was vicious, relentless, and highly profitable, eventually sparking a global corporate war that would be fought on several fronts: from living rooms and schoolyards to boardrooms and Congress. It was a once-in-a-lifetime, no-holds-barred conflict that pitted brother against brother, kid against adult, Sonic against Mario, and the US against Japan.Based on over two hundred interviews with former Sega and Nintendo employees, Console Wars is the underdog tale of how Kalinske miraculously turned an industry punchline into a market leader. It's the story of how a humble family man, with an extraordinary imagination and a gift for turning problems into competitive advantages, inspired a team of underdogs to slay a giant and, as a result, birth a $60 billion dollar industry.

    This long-awaited revision contains changes throughout the text. There are new implementations of most of the major programming systems in the book, including the interpreters and compilers, and the authors have incorporated many small changes that reflect their experience teaching the course at MIT since the first edition was published. A new theme has been introduced that emphasizes the central role played by different approaches to dealing with time in computational models: objects with state, concurrent programming, functional programming and lazy evaluation, and nondeterministic programming. There are new example sections on higher-order procedures in graphics and on applications of stream processing in numerical programming, and many new exercises. In addition, all the programs have been reworked to run in any Scheme implementation that adheres to the IEEE standard.

    It is the technology behind photo tagging systems at Facebook and Google, self-driving cars, speech recognition systems on your smartphone, and much more.In particular, Deep learning excels at solving machine perception problems: understanding the content of image data, video data, or sound data. Here's a simple example: say you have a large collection of images, and that you want tags associated with each image, for example, "dog," "cat," etc. Deep learning can allow you to create a system that understands how to map such tags to images, learning only from examples. This system can then be applied to new images, automating the task of photo tagging. A deep learning model only has to be fed examples of a task to start generating useful results on new data.

    So, why does conventional wisdom insist that software engineers focus primarily on the design and development of large-scale computing systems?In this collection of essays and articles, key members of Google's Site Reliability Team explain how and why their commitment to the entire lifecycle has enabled the company to successfully build, deploy, monitor, and maintain some of the largest software systems in the world. You'll learn the principles and practices that enable Google engineers to make systems more scalable, reliable, and efficient--lessons directly applicable to your organization.This book is divided into four sections: Introduction--Learn what site reliability engineering is and why it differs from conventional IT industry practicesPrinciples--Examine the patterns, behaviors, and areas of concern that influence the work of a site reliability engineer (SRE)Practices--Understand the theory and practice of an SRE's day-to-day work: building and operating large distributed computing systemsManagement--Explore Google's best practices for training, communication, and meetings that your organization can use