The twelve-factor app is a methodology for building software-as-a-service apps that: - Use declarative formats for setup automation, to minimize time and cost for new developers joining the project; - Have a clean contract with the underlying operating system, offering maximum portability between execution environments; - Are suitable for deployment on modern cloud platforms, obviating the need for servers and systems administration; - Minimize divergence between development and production, enabling continuous deployment for maximum agility; - And can scale up without significant changes to tooling, architecture, or development practices.The twelve-factor methodology can be applied to apps written in any programming language, and which use any combination of backing services (database, queue, memory cache, etc).

    Kai-Fu Lee—one of the world’s most respected experts on AI and China—reveals that China has suddenly caught up to the US at an astonishingly rapid and unexpected pace.In AI Superpowers, Kai-Fu Lee argues powerfully that because of these unprecedented developments in AI, dramatic changes will be happening much sooner than many of us expected. Indeed, as the US-Sino AI competition begins to heat up, Lee urges the US and China to both accept and to embrace the great responsibilities that come with significant technological power.Most experts already say that AI will have a devastating impact on blue-collar jobs. But Lee predicts that Chinese and American AI will have a strong impact on white-collar jobs as well. Is universal basic income the solution? In Lee’s opinion, probably not.  But he provides a clear description of which jobs will be affected and how soon, which jobs can be enhanced with AI, and most importantly, how we can provide solutions to some of the most profound changes in human history that are coming soon.

    Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package—PMTK (probabilistic modeling toolkit)—that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

    Due to their monopoly position in web browsers, and the fact web browsers have spread from PCs to phones, tablets and TVs; their status will continue to grow and grow. Despite their success, many software engineers are apprehensive about JavaScript and HTML. This apprehensiveness is not completely unfounded; both JavaScript and HTML were rushed in their early years, and driven by commercial rather than engineering interests. As a result, many dubious features crept into these languages. Due to backwards compatibility concerns, most of these features still remain. In addition, many software engineers have used these languages without ever learning them. JavaScript and HTML have low barriers to entry, and this, along with their similarity to other languages, led many software engineers to conclude that there really was nothing much to learn. If you have not used JavaScript and HTML for a number of years, or if you are a programmer or software engineer using other languages, you may be surprised at what they now offer. Browser based web applications are now capable of matching or exceeding the sophistication and scale of traditional desktop applications. In order to create complex web applications however, it is essential to learn these languages. This book takes the point of view that once you have a strong grasp of the fundamentals, the details will take care of themselves. It will not present you with long lists of APIs, or intricate details of every attribute, these can be found in reference manuals. It will focus on the details of each language that are fundamental to understanding how they work. This book will guide you through the process of developing a web application using HTML5, Javascript, jQuery and CSS. It contains the following content: 1. An introduction to the HTML5 markup language, and how it differs from HTML4 and XHTML. 2. An introduction to JavaScript, including an in-depth look at its use of objects and functions, along with the design patterns that support the development of robust web applications. 3. An introduction to jQuery selection, traversal, manipulation and events. 4. An in-depth look at the Web storage and IndexedDB APIs for client side data storage. 5. A guide to implementing offline web applications with the Application Cache API. 6. An introduction to the ways JavaScript can interact with the users file-system using the FileReader API. 7. The use of Web Workers in a web application to execute algorithms on background threads. 8. An introduction to AJAX, and the jQuery API supporting AJAX. 9. An introduction to Server Sent Events and Web Sockets. All subjects are introduced in the context of a sample web application. This book is intended for anyone with at least a superficial knowledge of HTML and programming.

    You'll learn pragmatic ways of approaching the development process and your personal coding techniques. You'll learn about your own attitudes, issues with working on a team, and how to best manage your learning, all in an iterative, incremental, agile style. You'll see how to apply each practice, and what benefits you can expect. Bottom line: This book will make you a better developer.

    This updated edition includes all the changes in Python 3, itself a significant shift from earlier versions of Python.The book begins with basic but useful programs that teach the core features of syntax, control flow, and data structures. It then moves to larger applications involving code management, object-oriented programming, web development, and converting code from earlier versions of Python.True to his audience of experienced developers, the author covers common programming language features concisely, while giving more detail to those features unique to Python.Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book.

    To grow professionally, you also need soft skills and effective learning techniques. Honing those skills is what this book is all about. Authors Dave Hoover and Adewale Oshineye have cataloged dozens of behavior patterns to help you perfect essential aspects of your craft. Compiled from years of research, many interviews, and feedback from O'Reilly's online forum, these patterns address difficult situations that programmers, administrators, and DBAs face every day. And it's not just about financial success. Apprenticeship Patterns also approaches software development as a means to personal fulfillment. Discover how this book can help you make the best of both your life and your career. Solutions to some common obstacles that this book explores in-depth include:Burned out at work? "Nurture Your Passion" by finding a pet project to rediscover the joy of problem solving.Feeling overwhelmed by new information? Re-explore familiar territory by building something you've built before, then use "Retreat into Competence" to move forward again.Stuck in your learning? Seek a team of experienced and talented developers with whom you can "Be the Worst" for a while. "Brilliant stuff! Reading this book was like being in a time machine that pulled me back to those key learning moments in my career as a professional software developer and, instead of having to learn best practices the hard way, I had a guru sitting on my shoulder guiding me every step towards master craftsmanship. I'll certainly be recommending this book to clients. I wish I had this book 14 years ago!" -Russ Miles, CEO, OpenCredo

    In a 1995 interview, the late Steve Jobs claimed that the secret to his and Apple’s success was talent. “We’ve gone to exceptional lengths to hire the best people,” he said, believing that the talented resource was twenty-five times more valuable than an average alternative. For Microsoft founder Bill Gates, the multiple was even higher:A great lathe operator commands several times the wage of an average lathe operator, but a great writer of software code is worth 10,000 times the price of an average software writer.While the actual number might be up for debate, the importance of technical talent is not. The most successful companies today are those that understand the strategic role that developers will play in their success or failure. Not just successful technology companies – virtually every company today needs a developer strategy. There’s a reason that ESPN and Sears have rolled out API programs, that companies are being bought not for their products but their people. The reason is that developers are the most valuable resource in business.How did we get here? How did developers become the most important constituency in business seemingly overnight? The New Kingmakers explores the rise of the developer class, its implications and provides suggestions for navigating the new developer-centric landscape.

    Digital technologies—with hardware, software, and networks at their core—will in the near future diagnose diseases more accurately than doctors can, apply enormous data sets to transform retailing, and accomplish many tasks once considered uniquely human.In The Second Machine Age MIT’s Erik Brynjolfsson and Andrew McAfee—two thinkers at the forefront of their field—reveal the forces driving the reinvention of our lives and our economy. As the full impact of digital technologies is felt, we will realize immense bounty in the form of dazzling personal technology, advanced infrastructure, and near-boundless access to the cultural items that enrich our lives.Amid this bounty will also be wrenching change. Professions of all kinds—from lawyers to truck drivers—will be forever upended. Companies will be forced to transform or die. Recent economic indicators reflect this shift: fewer people are working, and wages are falling even as productivity and profits soar.Drawing on years of research and up-to-the-minute trends, Brynjolfsson and McAfee identify the best strategies for survival and offer a new path to prosperity. These include revamping education so that it prepares people for the next economy instead of the last one, designing new collaborations that pair brute processing power with human ingenuity, and embracing policies that make sense in a radically transformed landscape.A fundamentally optimistic book, The Second Machine Age alters how we think about issues of technological, societal, and economic progress.

    Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications.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 when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability.The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.

    And considering that the "one man" is Neal Stephenson, "the hacker Hemingway" (Newsweek) -- acclaimed novelist, pragmatist, seer, nerd-friendly philosopher, and nationally bestselling author of groundbreaking literary works (Snow Crash, Cryptonomicon, etc., etc.) -- the word is well worth hearing. Mostly well-reasoned examination and partial rant, Stephenson's In the Beginning... was the Command Line is a thoughtful, irreverent, hilarious treatise on the cyber-culture past and present; on operating system tyrannies and downloaded popular revolutions; on the Internet, Disney World, Big Bangs, not to mention the meaning of life itself.

    The first edition of this book, Programming Perl, hit the shelves in 1990, and was quickly adopted as the undisputed bible of the language. Since then, Perl has grown with the times, and so has this book.Programming Perl is not just a book about Perl. It is also a unique introduction to the language and its culture, as one might expect only from its authors. Larry Wall is the inventor of Perl, and provides a unique perspective on the evolution of Perl and its future direction. Tom Christiansen was one of the first champions of the language, and lives and breathes the complexities of Perl internals as few other mortals do. Jon Orwant is the editor of The Perl Journal, which has brought together the Perl community as a common forum for new developments in Perl.Any Perl book can show the syntax of Perl's functions, but only this one is a comprehensive guide to all the nooks and crannies of the language. Any Perl book can explain typeglobs, pseudohashes, and closures, but only this one shows how they really work. Any Perl book can say that my is faster than local, but only this one explains why. Any Perl book can have a title, but only this book is affectionately known by all Perl programmers as "The Camel."This third edition of Programming Perl has been expanded to cover version 5.6 of this maturing language. New topics include threading, the compiler, Unicode, and other new features that have been added since the previous edition.

    This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree- based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

    Distinguishing agents (e.g., molecules, cells, animals, and species) from their interactions (e.g., chemical reactions, immune system responses, sexual reproduction, and evolution), Flake argues that it is the computational properties of interactions that account for much of what we think of as beautiful and interesting. From this basic thesis, Flake explores what he considers to be today's four most interesting computational topics: fractals, chaos, complex systems, and adaptation.Each of the book's parts can be read independently, enabling even the casual reader to understand and work with the basic equations and programs. Yet the parts are bound together by the theme of the computer as a laboratory and a metaphor for understanding the universe. The inspired reader will experiment further with the ideas presented to create fractal landscapes, chaotic systems, artificial life forms, genetic algorithms, and artificial neural networks.

    Software Craftsmanship transcends software engineering, demonstrating that quality software can't simply be manufactured: it must be built by craftspeople with pride in their work, and a personal commitment to excellence. In Software Craftsmanship, Pete McBreen focuses on the craft of software development, explaining why current software engineering techniques often fail, and offering programmers a new path to excellence. Just as the modern carpenter benefits from better tools, materials, and understanding, the modern programmer can benefit from better computers, reusable components, and more robust languages -- but only if he or she is prepared to treat the software profession as a true craft. McBreen explains what software craftsmanship means, how its affects users, and how it changes the developer's relationship with customers. He introduces the concepts of software apprentices and journeymen, shows what can (and can't) be learned from the software engineering movement, and presents specific steps you can take now to move towards craftsmanship in your work -- and your organization.