In clear, understandable language, Jourdian expertly guides the reader through a continuum of musical experience: sound, tone, melody, harmony, rhythm, composition, performance, listening, understanding—and finally to ecstasy. Along the way, a fascinating cast of characters brings Jourdian's narrative to vivid life: "idiots savants" who absorb whole pieces on a single hearing, composers who hallucinate entire compositions, a psychic who claims to take dictation from long-dead composers, and victims of brain damage who can move only when they hear music. Here is a book that will entertain, inform, and stimulate everyone who loves music—and make them think about their favorite song in startling new ways.

    It promised to shed light on the deep mysteries of consciousness, as well as offer relief to addicts and the mentally ill. But in the 1960s, with the vicious backlash against the counter-culture, all further research was banned. In recent years, however, work has quietly begun again on the amazing potential of LSD, psilocybin and DMT. Could these drugs in fact improve the lives of many people? Diving deep into this extraordinary world and putting himself forward as a guinea-pig, Michael Pollan has written a remarkable history of psychedelics and a compelling portrait of the new generation of scientists fascinated by the implications of these drugs. How to Change Your Mind is a report from what could very well be the future of human consciousness.

    In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases

    Unveiling here the kind of cutting-edge research that has made them giants in their fields, Daniel Goleman and Richard Davidson show us the truth about what meditation can really do for us, as well as exactly how to get the most out of it.Sweeping away common misconceptions and neuromythology to open readers' eyes to the ways data has been distorted to sell mind-training methods, the authors demonstrate that beyond the pleasant states mental exercises can produce, the real payoffs are the lasting personality traits that can result. But short daily doses will not get us to the highest level of lasting positive change--even if we continue for years--without specific additions. More than sheer hours, we need smart practice, including crucial ingredients such as targeted feedback from a master teacher and a more spacious, less attached view of the self, all of which are missing in widespread versions of mind training. The authors also reveal the latest data from Davidson's own lab that point to a new methodology for developing a broader array of mind-training methods with larger implications for how we can derive the greatest benefits from the practice.Exciting, compelling, and grounded in new research, this is one of those rare books that has the power to change us at the deepest level.

    These topics lie at the heart of many exciting areas of contemporary science and engineering - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics, and cryptography. This textbook introduces theory in tandem with applications. Information theory is taught alongside practical communication systems, such as arithmetic coding for data compression and sparse-graph codes for error-correction. A toolbox of inference techniques, including message-passing algorithms, Monte Carlo methods, and variational approximations, are developed alongside applications of these tools to clustering, convolutional codes, independent component analysis, and neural networks. The final part of the book describes the state of the art in error-correcting codes, including low-density parity-check codes, turbo codes, and digital fountain codes -- the twenty-first century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, David MacKay's groundbreaking book is ideal for self-learning and for undergraduate or graduate courses. Interludes on crosswords, evolution, and sex provide entertainment along the way. In sum, this is a textbook on information, communication, and coding for a new generation of students, and an unparalleled entry point into these subjects for professionals in areas as diverse as computational biology, financial engineering, and machine learning.

    Our memory is unreliable; we can't multiply large sums in our heads; advertising manipulates our judgment; we tend to distrust people who are different from us; supernatural beliefs and superstitions are hard to shake; we prefer instant gratification to long-term gain; and what we presume to be rational decisions are often anything but. Drawing on striking examples and fascinating studies, neuroscientist Dean Buonomano illuminates the causes and consequences of these "bugs" in terms of the brain's innermost workings and their evolutionary purposes. He then goes one step further, examining how our brains function-and malfunction-in the digital, predator-free, information-saturated, special effects-addled world that we have built for ourselves. Along the way, Brain Bugs gives us the tools to hone our cognitive strengths while recognizing our inherent weaknesses.

    Our brave new technologies offer incredible opportunities for work and play. But at what price?   Now renowned neuroscientist Susan Greenfield—known in the United Kingdom for challenging entrenched conventional views—brings together a range of scientific studies, news events, and cultural criticism to create an incisive snapshot of “the global now.” Disputing the assumption that our technologies are harmless tools, Greenfield explores whether incessant exposure to social media sites, search engines, and videogames is capable of rewiring our brains, and whether the minds of people born before and after the advent of the Internet differ.   Stressing the impact on Digital Natives—those who’ve never known a world without the Internet—Greenfield exposes how neuronal networking may be affected by unprecedented bombardments of audiovisual stimuli, how gaming can shape a chemical landscape in the brain similar to that in gambling addicts, how surfing the Net risks placing a premium on information rather than on deep knowledge and understanding, and how excessive use of social networking sites limits the maturation of empathy and identity.   But Mind Change also delves into the potential benefits of our digital lifestyle. Sifting through the cocktail of not only threat but opportunity these technologies afford, Greenfield explores how gaming enhances vision and motor control, how touch tablets aid students with developmental disabilities, and how political “clicktivism” foments positive change.   In a world where adults spend ten hours a day online, and where tablets are the common means by which children learn and play, Mind Change reveals as never before the complex physiological, social, and cultural ramifications of living in the digital age. A book that will be to the Internet what An Inconvenient Truth was to global warming, Mind Change is provocative, alarming, and a call to action to ensure a future in which technology fosters—not frustrates—deep thinking, creativity, and true fulfillment.

    With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you've found it.Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general -- all from information that you and others collect every day. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application. This book explains:Collaborative filtering techniques that enable online retailers to recommend products or media Methods of clustering to detect groups of similar items in a large dataset Search engine features -- crawlers, indexers, query engines, and the PageRank algorithm Optimization algorithms that search millions of possible solutions to a problem and choose the best one Bayesian filtering, used in spam filters for classifying documents based on word types and other features Using decision trees not only to make predictions, but to model the way decisions are made Predicting numerical values rather than classifications to build price models Support vector machines to match people in online dating sites Non-negative matrix factorization to find the independent features in a dataset Evolving intelligence for problem solving -- how a computer develops its skill by improving its own code the more it plays a game Each chapter includes exercises for extending the algorithms to make them more powerful. Go beyond simple database-backed applications and put the wealth of Internet data to work for you. "Bravo! I cannot think of a better way for a developer to first learn these algorithms and methods, nor can I think of a better way for me (an old AI dog) to reinvigorate my knowledge of the details."-- Dan Russell, Google "Toby's book does a great job of breaking down the complex subject matter of machine-learning algorithms into practical, easy-to-understand examples that can be directly applied to analysis of social interaction across the Web today. If I had this book two years ago, it would have saved precious time going down some fruitless paths."-- Tim Wolters, CTO, Collective Intellect

    He was also a memoirist who wrote with honesty and humor about the remarkable and strange encounters and experiences that shaped him (Uncle Tungsten, On the Move, Gratitude). Sacks, an Oxford-educated polymath, had a deep familiarity not only with literature and medicine but with botany, animal anatomy, chemistry, the history of science, philosophy, and psychology. The River of Consciousness is one of two books Sacks was working on up to his death, and it reveals his ability to make unexpected connections, his sheer joy in knowledge, and his unceasing, timeless project to understand what makes us human.

    Now in paperback, Dr Jeffrey Schwartz and Sharon Begley's groundbreaking work, The Mind and the Brain, argues exactly the opposite: that the mind has a life of its own.Dr Schwartz, a leading researcher in brain dysfunctions, and Wall Street Journal science columnist Sharon Begley demonstrate that the human mind is an independent entity that can shape and control the functioning of the physical brain. Their work has its basis in our emerging understanding of adult neuroplasticity–the brain's ability to be rewired not just in childhood, but throughout life, a trait only recently established by neuroscientists.Through decades of work treating patients with obsessive–compulsive disorder (OCD), Schwartz made an extraordinary finding: while following the therapy he developed, his patients were effecting significant and lasting changes in their own neural pathways. It was a scientific first: by actively focusing their attention away from negative behaviors and toward more positive ones, Schwartz's patients were using their minds to reshape their brains–and discovering a thrilling new dimension to the concept of neuroplasticity.The Mind and the Brain follows Schwartz as he investigates this newly discovered power, which he calls self–directed neuroplasticity or, more simply, mental force. It describes his work with noted physicist Henry Stapp and connects the concept of 'mental force' with the ancient practice of mindfulness in Buddhist tradition. And it points to potential new applications that could transform the treatment of almost every variety of neurological dysfunction, from dyslexia to stroke–and could lead to new strategies to help us harness our mental powers. Yet as wondrous as these implications are, perhaps even more important is the philosophical dimension of Schwartz's work. For the existence of mental force offers convincing scientific evidence of human free will, and thus of man's inherent capacity for moral choice.

    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.

    It is the basis for a new approach to artificial intelligence that aims to program computers to use example data or past experience to solve a given problem. In this volume in the MIT Press Essential Knowledge series, Ethem Alpayd�n offers a concise and accessible overview of the new AI. This expanded edition offers new material on such challenges facing machine learning as privacy, security, accountability, and bias. Alpayd�n, author of a popular textbook on machine learning, explains that as Big Data has gotten bigger, the theory of machine learning--the foundation of efforts to process that data into knowledge--has also advanced. He describes the evolution of the field, explains important learning algorithms, and presents example applications. He discusses the use of machine learning algorithms for pattern recognition; artificial neural networks inspired by the human brain; algorithms that learn associations between instances; and reinforcement learning, when an autonomous agent learns to take actions to maximize reward. In a new chapter, he considers transparency, explainability, and fairness, and the ethical and legal implications of making decisions based on data.

    Why? Hypocrisy is the natural state of the human mind.Robert Kurzban shows us that the key to understanding our behavioral inconsistencies lies in understanding the mind's design. The human mind consists of many specialized units designed by the process of evolution by natural selection. While these modules sometimes work together seamlessly, they don't always, resulting in impossibly contradictory beliefs, vacillations between patience and impulsiveness, violations of our supposed moral principles, and overinflated views of ourselves.This modular, evolutionary psychological view of the mind undermines deeply held intuitions about ourselves, as well as a range of scientific theories that require a self with consistent beliefs and preferences. Modularity suggests that there is no I. Instead, each of us is a contentious we--a collection of discrete but interacting systems whose constant conflicts shape our interactions with one another and our experience of the world.In clear language, full of wit and rich in examples, Kurzban explains the roots and implications of our inconsistent minds, and why it is perfectly natural to believe that everyone else is a hypocrite.

    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.

    With his classic Why We Get Sick, Dr. Randolph Nesse helped to establish the field of evolutionary medicine. Now he returns with a book that transforms our understanding of mental disorders by exploring a fundamentally new question. Instead of asking why certain people suffer from mental illness, Nesse asks why natural selection has left us all with fragile minds.Drawing on revealing stories from his own clinical practice and insights from evolutionary biology, Nesse shows how negative emotions are useful in certain situations, yet can become overwhelming. Anxiety protects us from harm in the face of danger, but false alarms are inevitable. Low moods prevent us from wasting effort in pursuit of unreachable goals, but they often escalate into pathological depression. Other mental disorders, such as addiction and anorexia, result from the mismatch between modern environment and our ancient human past. And there are good evolutionary reasons for sexual disorders and for why genes for schizophrenia persist. Taken together, these and many more insights help to explain the pervasiveness of human suffering, and show us new paths for relieving it by understanding individuals as individuals.