Collected from the series of blog posts starting at: https://bartoszmilewski.com/2014/10/2...Hardcover available at: http://www.blurb.com/b/9008339-catego...

    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.

    However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation. Similarly, new models based on kernels have had a significant impact on both algorithms and applications. This new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners, and assumes no previous knowledge of pattern recognition or machine learning concepts. Knowledge of multivariate calculus and basic linear algebra is required, and some familiarity with probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

    This cycle is at the root of all addictions, addictions to drugs, sex, love, cigarettes, soap operas, wealth, and wisdom itself. But why should this be so? Why are we desperate for what we don't have, or can't have, often at great cost to what we do have, thereby risking our peace and contentment, our safety, and even our lives?"The answer, says Dr. Marc Lewis, lies in the structure and function of the human brain. Marc Lewis is a distinguished neuroscientist. And, for many years, he was a drug addict himself, dependent on a series of dangerous substances, from LSD to heroin. His narrative moves back and forth between the often dark, compellingly recounted story of his relationship with drugs and a revelatory analysis of what was going on in his brain. He shows how drugs speak to the brain - which is designed to seek rewards and soothe pain - in its own language. He shows in detail the neural mechanics of a variety of powerful drugs and of the onset of addiction, itself a distortion of normal perception.Dr. Lewis freed himself from addiction and ended up studying it. At the age of 30 he traded in his pharmaceutical supplies for the life of a graduate student, eventually becoming a professor of developmental psychology, and then of neuroscience - his field for the last 12 years. This is the story of his journey, seen from the inside out.

    By extracting the fundamental concepts from this enormous and ever-growing field, the authors tell the story of cell biology, and create a coherent framework through which non-expert readers may approach the subject. Written in clear and concise language, and beautifully illustrated, the book is enjoyable to read, and it provides a clear sense of the excitement of modern biology. Molecular Biology of the Cell sets forth the current understanding of cell biology (completely updated as of Autumn 2001), and it explores the intriguing implications and possibilities of the great deal that remains unknown. The hallmark features of previous editions continue in the Fourth Edition. The book is designed with a clean and open, single-column layout. The art program maintains a completely consistent format and style, and includes over 1,600 photographs, electron micrographs, and original drawings by the authors. Clear and concise concept headings introduce each section. Every chapter contains extensive references. Most important, every chapter has been subjected to a rigorous, collaborative revision process where, in addition to incorporating comments from expert reviewers, each co-author reads and reviews the other authors' prose. The result is a truly integrated work with a single authorial voice. Features : - Places the latest hot topics sensibly in context - including genomics, protein structure, array technology, stem cells and genetics diseases. - Incorporates and emphasises new genomic data. - All of molecular biology is brought together into one section (chapters 4-7) covering classically defined molecular biology and molecular genetics. - Two chapters deal exclusively with methods and contain information on the latest tools and techniques. - New chapters on "Pathogens, Infection, and Innate Immunity". - Cell Biology Interactive CD-ROM is packaged with every copy of the book. - Contains over 1,600 illustrations, electron micrographs and photographs, of which over 1,000 are originally conceived by the authors.

    While studying brain scans of several family members, he discovered that one perfectly matched a pattern he d found in the brains of serial killers. This meant one of two things: Either his family s scans had been mixed up with those of felons or someone in his family was a psychopath.Even more disturbing: The scan in question was his own.This is Fallon s account of coming to grips with this discovery and its implications. How could he, a happy family man who had never been prone to violence, be a psychopath? How much did his biology influence his behavior?Fallon shares his journey to answer these questions and the discoveries that ultimately led to his conclusion: Despite everything science can teach, humans are even more complex than we can imagine."

    In On Being Certain, neurologist Robert Burton challenges the notions of how we think about what we know. He shows that the feeling of certainty we have when we know something comes from sources beyond our control and knowledge. In fact, certainty is a mental sensation, rather than evidence of fact. Because this feeling of knowing seems like confirmation of knowledge, we tend to think of it as a product of reason. But an increasing body of evidence suggests that feelings such as certainty stem from primitive areas of the brain, and are independent of active, conscious reflection and reasoning. The feeling of knowing happens to us; we cannot make it happen. Bringing together cutting edge neuroscience, experimental data, and fascinating anecdotes, Robert Burton explores the inconsistent and sometimes paradoxical relationship between our thoughts and what we actually know. Provocative and groundbreaking, On Being Certain, will challenge what you know (or think you know) about the mind, knowledge, and reason.

    In The Master Algorithm, Pedro Domingos lifts the veil to give us a peek inside the learning machines that power Google, Amazon, and your smartphone. He assembles a blueprint for the future universal learner--the Master Algorithm--and discusses what it will mean for business, science, and society. If data-ism is today's philosophy, this book is its bible.

    Rahul Jandial is on the cutting edge of the latest advancements in neuroscience. This fascinating book draws on Dr. Jandial’s broad-spectrum expertise and brings together the best of various fields-–surgery, science, brain structure, the conscious mind–-all to explain the bigger picture of brain health and rejuvenation. It is a journey into his operating room, around the world on his surgical missions, inside his laboratory, and to the outer edges of neuroscience to reveal the latest brain breakthroughs that are turning science fiction into reality, translating their implications for everyday life. Busting myths along the way, Jandial helps readers get wired for success at work and school, perform better when the pressure is on, boost memory, control stress and emotions, minimize pain, stick to a healthy eating plan, unleash creativity, raise smarter kids, and stay sharp as they age. Combining the treatment guidelines he gives his patients, the most promising concepts from frontier science, and the smartest super-achiever hacks, he provides practical takeaways for optimizing brain function and leading a healthier, happier, more productive life.

    Now this classic book has been fully updated and revised with leading-edge practices--and hundreds of new code samples--illustrating the art and science of software construction. Capturing the body of knowledge available from research, academia, and everyday commercial practice, McConnell synthesizes the most effective techniques and must-know principles into clear, pragmatic guidance. No matter what your experience level, development environment, or project size, this book will inform and stimulate your thinking--and help you build the highest quality code. Discover the timeless techniques and strategies that help you: Design for minimum complexity and maximum creativity Reap the benefits of collaborative development Apply defensive programming techniques to reduce and flush out errors Exploit opportunities to refactor--or evolve--code, and do it safely Use construction practices that are right-weight for your project Debug problems quickly and effectively Resolve critical construction issues early and correctly Build quality into the beginning, middle, and end of your project

    It is also about Python. However, there is much more. The study of algorithms and data structures is central to understanding what computer science is all about. Learning computer science is not unlike learning any other type of difficult subject matter. The only way to be successful is through deliberate and incremental exposure to the fundamental ideas. A beginning computer scientist needs practice so that there is a thorough understanding before continuing on to the more complex parts of the curriculum. In addition, a beginner needs to be given the opportunity to be successful and gain confidence. This textbook is designed to serve as a text for a first course on data structures and algorithms, typically taught as the second course in the computer science curriculum. Even though the second course is considered more advanced than the first course, this book assumes you are beginners at this level. You may still be struggling with some of the basic ideas and skills from a first computer science course and yet be ready to further explore the discipline and continue to practice problem solving. We cover abstract data types and data structures, writing algorithms, and solving problems. We look at a number of data structures and solve classic problems that arise. The tools and techniques that you learn here will be applied over and over as you continue your study of computer science.

     At any given moment, somewhere in the world someone struggles with the same software design problems you have. You know you don't want to reinvent the wheel (or worse, a flat tire), so you look to Design Patterns--the lessons learned by those who've faced the same problems. With Design Patterns, you get to take advantage of the best practices and experience of others, so that you can spend your time on...something else. Something more challenging. Something more complex. Something more fun. You want to learn about the patterns that matter--why to use them, when to use them, how to use them (and when NOT to use them). But you don't just want to see how patterns look in a book, you want to know how they look "in the wild". In their native environment. In other words, in real world applications. You also want to learn how patterns are used in the Java API, and how to exploit Java's built-in pattern support in your own code. You want to learn the real OO design principles and why everything your boss told you about inheritance might be wrong (and what to do instead). You want to learn how those principles will help the next time you're up a creek without a design pattern. Most importantly, you want to learn the "secret language" of Design Patterns so that you can hold your own with your co-worker (and impress cocktail party guests) when he casually mentions his stunningly clever use of Command, Facade, Proxy, and Factory in between sips of a martini. You'll easily counter with your deep understanding of why Singleton isn't as simple as it sounds, how the Factory is so often misunderstood, or on the real relationship between Decorator, Facade and Adapter. With Head First Design Patterns, you'll avoid the embarrassment of thinking Decorator is something from the "Trading Spaces" show. Best of all, in a way that won't put you to sleep! We think your time is too important (and too short) to spend it struggling with academic texts. If you've read a Head First book, you know what to expect--a visually rich format designed for the way your brain works. Using the latest research in neurobiology, cognitive science, and learning theory, Head First Design Patterns will load patterns into your brain in a way that sticks. In a way that lets you put them to work immediately. In a way that makes you better at solving software design problems, and better at speaking the language of patterns with others on your team.

    Wolfram lets the world see his work in A New Kind of Science, a gorgeous, 1,280-page tome more than a decade in the making. With patience, insight, and self-confidence to spare, Wolfram outlines a fundamental new way of modeling complex systems. On the frontier of complexity science since he was a boy, Wolfram is a champion of cellular automata--256 "programs" governed by simple nonmathematical rules. He points out that even the most complex equations fail to accurately model biological systems, but the simplest cellular automata can produce results straight out of nature--tree branches, stream eddies, and leopard spots, for instance. The graphics in A New Kind of Science show striking resemblance to the patterns we see in nature every day. Wolfram wrote the book in a distinct style meant to make it easy to read, even for nontechies; a basic familiarity with logic is helpful but not essential. Readers will find themselves swept away by the elegant simplicity of Wolfram's ideas and the accidental artistry of the cellular automaton models. Whether or not Wolfram's revolution ultimately gives us the keys to the universe, his new science is absolutely awe-inspiring. --Therese Littleton

    How can it be possible that the world we see is not objective reality? And how can our senses be useful if they are not communicating the truth? Hoffman grapples with these questions and more over the course of this eye-opening work.Ever since Homo sapiens has walked the earth, natural selection has favored perception that hides the truth and guides us toward useful action, shaping our senses to keep us alive and reproducing. We observe a speeding car and do not walk in front of it; we see mold growing on bread and do not eat it. These impressions, though, are not objective reality. Just like a file icon on a desktop screen is a useful symbol rather than a genuine representation of what a computer file looks like, the objects we see every day are merely icons, allowing us to navigate the world safely and with ease.The real-world implications for this discovery are huge. From examining why fashion designers create clothes that give the illusion of a more “attractive” body shape to studying how companies use color to elicit specific emotions in consumers, and even dismantling the very notion that spacetime is objective reality, The Case Against Reality dares us to question everything we thought we knew about the world we see.

    The human brain has some capabilities that the brains of other animals lack. It is to these distinctive capabilities that our species owes its dominant position. If machine brains surpassed human brains in general intelligence, then this new superintelligence could become extremely powerful--possibly beyond our control. As the fate of the gorillas now depends more on humans than on the species itself, so would the fate of humankind depend on the actions of the machine superintelligence.But we have one advantage: we get to make the first move. Will it be possible to construct a seed Artificial Intelligence, to engineer initial conditions so as to make an intelligence explosion survivable? How could one achieve a controlled detonation?