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.

    It took the open source world by storm since its inception in 2005, and is used by small development shops and giants like Google, Red Hat, and IBM, and of course many open source projects.A book by Git experts to turn you into a Git expert. Introduces the world of distributed version control Shows how to build a Git development workflow.

    Indeed, the brain's power is being confirmed every day in new studies and research. But there is a brain we don't generally hear about, a brain we might not want to hear about…the "prima donna within."Exposing the mind's deceptions and exploring how the mind defends and glorifies the ego, Dr. Cordelia Fine illustrates the brain's tendency to self-delusion. Whether it be hindsight bias, wishful thinking, unrealistic optimism, or moral excuse-making, each of us has a slew of inborn mind-bugs and ordinary prejudices that prevent us from seeing the truth about the world and ourselves. With fascinating studies to support her arguments, Dr. Fine takes us on an insightful, rip-roaringly funny tour through the brain you never knew you had.

    Republished in book form shortly thereafter, it has since gone through four hardcover and sixteen paperback printings. It is a revolutionary work, astounding in its foresight and contemporaneity. The University of Illinois Press is pleased and honored to issue this commemorative reprinting of a classic.

    In this direct and non-technical discussion of consciousness, Dr. Gerald M. Edelman draws on a lifetime of scientific inquiry into the workings of the brain to formulate answers to the mind-body questions that intrigue every thinking person.Concise and understandable, the book explains pertinent findings of modern neuroscience and describes how consciousness arises in complex brains. Edelman explores the relation of consciousness to causation, to evolution, to the development of the self, and to the origins of feelings, learning, and memory. His analysis of the brain activities underlying consciousness is based on recent remarkable advances in biochemistry, immunology, medical imaging, neuroscience, and evolutionary biology, yet the implications of his book extend farther—beyond the worlds of science and medicine into virtually every area of human inquiry.

    By presenting actual experiences and analyzing them as they are described, the author conveys the developmental thought processes employed and shows a style of thinking that leads to successful results is something that can be learned. Along with spectacular successes, the author also conveys how failures contributed to shaping the thought processes. Provides the reader with a style of thinking that will enhance a person's ability to function as a problem-solver of complex technical issues. Consists of a collection of stories about the author's participation in significant discoveries, relating how those discoveries came about and, most importantly, provides analysis about the thought processes and reasoning that took place as the author and his associates progressed through engineering problems.

    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.

    It argues that evolution designed us both to teach and learn, and that the drive to learn is our most important instinct. It also reveals as fascinating insights about our adult capacities and how even young children -- as well as adults -- use some of the same methods that allow scientists to learn so much about the world. Filled with surprise at every turn, this vivid, lucid, and often funny book gives us a new view of the inner life of children and the mysteries of the mind.

    But as scientists break open the mind’s black box with the latest tools of neuroscience, they’re discovering that this is not how the mind works. Our best decisions are a finely tuned blend of both feeling and reason—and the precise mix depends on the situation. When buying a house, for example, it’s best to let our unconscious mull over the many variables. But when we’re picking a stock, intuition often leads us astray. The trick is to determine when to use the different parts of the brain, and to do this, we need to think harder (and smarter) about how we think.Jonah Lehrer arms us with the tools we need, drawing on cutting-edge research as well as the real-world experiences of a wide range of “deciders”—from airplane pilots and hedge fund investors to serial killers and poker players.Lehrer shows how people are taking advantage of the new science to make better television shows, win more football games, and improve military intelligence. His goal is to answer two questions that are of interest to just about anyone, from CEOs to firefighters: How does the human mind make decisions? And how can we make those decisions better?

    Sapolsky's storytelling concept is delightful but it also has a powerful intrinsic logic: he starts by looking at the factors that bear on a person's reaction in the precise moment a behavior occurs, and then hops back in time from there, in stages, ultimately ending up at the deep history of our species and its evolutionary legacy.And so the first category of explanation is the neurobiological one. A behavior occurs--whether an example of humans at our best, worst, or somewhere in between. What went on in a person's brain a second before the behavior happened? Then Sapolsky pulls out to a slightly larger field of vision, a little earlier in time: What sight, sound, or smell caused the nervous system to produce that behavior? And then, what hormones acted hours to days earlier to change how responsive that individual is to the stimuli that triggered the nervous system? By now he has increased our field of vision so that we are thinking about neurobiology and the sensory world of our environment and endocrinology in trying to explain what happened.Sapolsky keeps going: How was that behavior influenced by structural changes in the nervous system over the preceding months, by that person's adolescence, childhood, fetal life, and then back to his or her genetic makeup? Finally, he expands the view to encompass factors larger than one individual. How did culture shape that individual's group, what ecological factors millennia old formed that culture? And on and on, back to evolutionary factors millions of years old.The result is one of the most dazzling tours d'horizon of the science of human behavior ever attempted, a majestic synthesis that harvests cutting-edge research across a range of disciplines to provide a subtle and nuanced perspective on why we ultimately do the things we do...for good and for ill. Sapolsky builds on this understanding to wrestle with some of our deepest and thorniest questions relating to tribalism and xenophobia, hierarchy and competition, morality and free will, and war and peace. Wise, humane, often very funny, Behave is a towering achievement, powerfully humanizing, and downright heroic in its own right.

    The book will teach you about:* Neural networks, a beautiful biologically-inspired programming paradigm which enables a computer to learn from observational data* Deep learning, a powerful set of techniques for learning in neural networksNeural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing. This book will teach you the core concepts behind neural networks and deep learning.

    The computer programmer, the doctor, the artist, and even the parent and citizen all engage in a craftsman’s work. In this thought-provoking book, Sennett explores the work of craftsmen past and present, identifies deep connections between material consciousness and ethical values, and challenges received ideas about what constitutes good work in today’s world. The Craftsman engages the many dimensions of skill—from the technical demands to the obsessive energy required to do good work. Craftsmanship leads Sennett across time and space, from ancient Roman brickmakers to Renaissance goldsmiths to the printing presses of Enlightenment Paris and the factories of industrial London; in the modern world he explores what experiences of good work are shared by computer programmers, nurses and doctors, musicians, glassblowers, and cooks. Unique in the scope of his thinking, Sennett expands previous notions of crafts and craftsmen and apprises us of the surprising extent to which we can learn about ourselves through the labor of making physical things.

    But developing these systems brings its own set of headaches. With lots of examples and practical advice, this book takes a holistic view of the topics that system architects and administrators must consider when building, managing, and evolving microservice architectures.Microservice technologies are moving quickly. Author Sam Newman provides you with a firm grounding in the concepts while diving into current solutions for modeling, integrating, testing, deploying, and monitoring your own autonomous services. You'll follow a fictional company throughout the book to learn how building a microservice architecture affects a single domain.Discover how microservices allow you to align your system design with your organization's goalsLearn options for integrating a service with the rest of your systemTake an incremental approach when splitting monolithic codebasesDeploy individual microservices through continuous integrationExamine the complexities of testing and monitoring distributed servicesManage security with user-to-service and service-to-service modelsUnderstand the challenges of scaling microservice architectures

    Increasingly, the decisions that affect our lives--where we go to school, whether we can get a job or a loan, how much we pay for health insurance--are being made not by humans, but by machines. In theory, this should lead to greater fairness: Everyone is judged according to the same rules.But as mathematician and data scientist Cathy O'Neil reveals, the mathematical models being used today are unregulated and uncontestable, even when they're wrong. Most troubling, they reinforce discrimination--propping up the lucky, punishing the downtrodden, and undermining our democracy in the process.

    This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python.You'll work with a case study throughout the book to help you learn the entire data analysis process—from collecting data and generating statistics to identifying patterns and testing hypotheses. Along the way, you'll become familiar with distributions, the rules of probability, visualization, and many other tools and concepts.Develop your understanding of probability and statistics by writing and testing codeRun experiments to test statistical behavior, such as generating samples from several distributionsUse simulations to understand concepts that are hard to grasp mathematicallyLearn topics not usually covered in an introductory course, such as Bayesian estimationImport data from almost any source using Python, rather than be limited to data that has been cleaned and formatted for statistics toolsUse statistical inference to answer questions about real-world data