A timeless introduction to the field and a landmark in symbolic logic, showing that classical logic can be treated algebraically.

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. 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.

Paperback Edition What do flashlights, the British invasion, black cats, and seesaws have to do with computers? In CODE, they show us the ingenious ways we manipulate language and invent new means of communicating with each other. And through CODE, we see how this ingenuity and our very human compulsion to communicate have driven the technological innovations of the past two centuries. Using everyday objects and familiar language systems such as Braille and Morse code, author Charles Petzold weaves an illuminating narrative for anyone who s ever wondered about the secret inner life of computers and other smart machines. It s a cleverly illustrated and eminently comprehensible story and along the way, you ll discover you ve gained a real context for understanding today s world of PCs, digital media, and the Internet. No matter what your level of technical savvy, CODE will charm you and perhaps even awaken the technophile within.

Written by one of the preeminent researchers in the field, this book provides a comprehensive exposition of modern analysis of causation. It shows how causality has grown from a nebulous concept into a mathematical theory with significant applications in the fields of statistics, artificial intelligence, economics, philosophy, cognitive science, and the health and social sciences. Judea Pearl presents and unifies the probabilistic, manipulative, counterfactual, and structural approaches to causation and devises simple mathematical tools for studying the relationships between causal connections and statistical associations. Cited in more than 2,100 scientific publications, it continues to liberate scientists from the traditional molds of statistical thinking. In this revised edition, Judea Pearl elucidates thorny issues, answers readers' questions, and offers a panoramic view of recent advances in this field of research. Causality will be of interest to students and professionals in a wide variety of fields. Dr Judea Pearl has received the 2011 Rumelhart Prize for his leading research in Artificial Intelligence (AI) and systems from The Cognitive Science Society.

In The Art and Science of Java, Stanford professor and well-known leader in CS Education Eric Roberts emphasizes the student-friendly exposition that led to the success of The Art and Science of C. By following the recommendations of the Association of Computing Machinery's Java Task Force, this first edition text adopts a modern objects-first approach that introduces students to useful hierarchies from the very beginning. Packages are translated into a minimally complex collection of pedagogical resources that make it easier to teach Java while retaining the language's industrial strength.

Product Description
Incorporating significant editorial changes from earlier editions, the fourth edition of Ludwig Wittgenstein's Philosophical Investigations is the definitive en face German-English version of the most important work of 20th-century philosophy
•The extensively revised English translation incorporates many hundreds of changes to Anscombes original translation
•Footnoted remarks in the earlier editions have now been relocated in the text
•What was previously referred to as Part 2 is now republished as Philosophy of Psychology A Fragment, and all the remarks in it are numbered for ease of reference
•New detailed editorial endnotes explain decisions of translators and identify references and allusions in Wittgenstein's original text
•Now features new essays on the history of the Philosophical Investigations, and the problems of translating Wittgensteins text

For courses in Image Processing and Computer Vision. Completely self-contained--and heavily illustrated--this introduction to basic concepts and methodologies for digital image processing is written at a level that truly is suitable for seniors and first-year graduate students in almost any technical discipline. The leading textbook in its field for more than twenty years, it continues its cutting-edge focus on contemporary developments in all mainstream areas of image processing--e.g., image fundamentals, image enhancement in the spatial and frequency domains, restoration, color image processing, wavelets, image compression, morphology, segmentation, image description, and the fundamentals of object recognition. It focuses on material that is fundamental and has a broad scope of application.

The C++11 standard allows programmers to express ideas more clearly, simply, and directly, and to write faster, more efficient code. Bjarne Stroustrup, the designer and original implementer of C++, thoroughly covers the details of this language and its use in his definitive reference,
. In
, Stroustrup excerpts the overview chapters from that complete reference, expanding and enhancing them to give an experienced programmer-in just a few hours-a clear idea of what constitutes modern C++. In this concise, self-contained guide, Stroustrup covers most major language features and the major standard-library components-not, of course, in great depth, but to a level that gives programmers a meaningful overview of the language, some key examples, and practical help in getting started. Stroustrup presents the C++ features in the context of the programming styles they support, such as object-oriented and generic programming. His tour is remarkably comprehensive. Coverage begins with the basics, then ranges widely through more advanced topics, including many that are new in C++11, such as move semantics, uniform initialization, lambda expressions, improved containers, random numbers, and concurrency. The tour ends with a discussion of the design and evolution of C++ and the extensions added for C++11. This guide does not aim to teach you how to program (see Stroustrup's
for that); nor will it be the only resource you'll need for C++ mastery (see Stroustrup's
, for that). If, however, you are a C or C++ programmer wanting greater familiarity with the current C++ language, or a programmer versed in another language wishing to gain an accurate picture of the nature and benefits of modern C++, you can't find a shorter or simpler introduction than this tour provides.

Neural networks are a computing paradigm that is finding increasing attention among computer scientists. In this book, theoretical laws and models previously scattered in the literature are brought together into a general theory of artificial neural nets. Always with a view to biology and starting with the simplest nets, it is shown how the properties of models change when more general computing elements and net topologies are introduced. Each chapter contains examples, numerous illustrations, and a bibliography. The book is aimed at readers who seek an overview of the field or who wish to deepen their knowledge. It is suitable as a basis for university courses in neurocomputing.

If you are new to programming with Python and are looking for a solid introduction, this is the book for you. Developed by computer science instructors, books in the "for the absolute beginner" series teach the principles of programming through simple game creation. You will acquire the skills that you need for practical Python programming applications and will learn how these skills can be put to use in real-world scenarios. Throughout the chapters, you will find code samples that illustrate concepts presented. At the end of each chapter, you will find a complete game that demonstrates the key ideas in the chapter, a summary of the chapter, and a set of challenges that tests your newfound knowledge. By the time you finish this book, you?ll be well versed in Python and be able to apply the basic programming principles you?ve learned to the next programming language you tackle.

Based on the authors' market leading data structures books in Java and C++, this book offers a comprehensive, definitive introduction to data structures in Python by authoritative authors. Data Structures and Algorithms in Python is the first authoritative object-oriented book available for Python data structures. Designed to provide a comprehensive introduction to data structures and algorithms, including their design, analysis, and implementation, the text will maintain the same general structure as Data Structures and Algorithms in Java and Data Structures and Algorithms in C++. Begins by discussing Python's conceptually simple syntax, which allows for a greater focus on concepts. Employs a consistent object-oriented viewpoint throughout the text. Presents each data structure using ADTs and their respective implementations and introduces important design patterns as a means to organize those implementations into classes, methods, and objects. Provides a thorough discussion on the analysis and design of fundamental data structures. Includes many helpful Python code examples, with source code provided on the website. Uses illustrations to present data structures and algorithms, as well as their analysis, in a clear, visual manner. Provides hundreds of exercises that promote creativity, help readers learn how to think like programmers, and reinforce important concepts. Contains many Python-code and pseudo-code fragments, and hundreds of exercises, which are divided into roughly 40% reinforcement exercises, 40% creativity exercises, and 20% programming projects.

Artificial "neural networks" are widely used as flexible models for classification and regression applications, but questions remain about how the power of these models can be safely exploited when training data is limited. This book demonstrates how Bayesian methods allow complex neural network models to be used without fear of the "overfitting" that can occur with traditional training methods. Insight into the nature of these complex Bayesian models is provided by a theoretical investigation of the priors over functions that underlie them. A practical implementation of Bayesian neural network learning using Markov chain Monte Carlo methods is also described, and software for it is freely available over the Internet. Presupposing only basic knowledge of probability and statistics, this book should be of interest to researchers in statistics, engineering, and artificial intelligence.

"Written by three experts in the field, Deep Learning is the only comprehensive book on the subject." -- Elon Musk, co-chair of OpenAI; co-founder and CEO of Tesla and SpaceX
Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. 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.

This title now updated: the definitive neuroscience resource-from Eric R. Kandel, MD (winner of the Nobel Prize in 2000); James H. Schwartz, MD, PhD; Thomas M. Jessell, PhD; Steven A. Siegelbaum, PhD; and A. J. Hudspeth, PhD 900 full-color illustrations. Deciphering the link between the human brain and behavior has always been one of the most intriguing - and often challenging-aspects of scientific endeavor. The sequencing of the human genome, and advances in molecular biology, have illuminated the pathogenesis of many neurological diseases and have propelled our knowledge of how the brain controls behavior. To grasp the wider implications of these developments and gain a fundamental understanding of this dynamic, fast-moving field, Principles of Neuroscience stands alone as the most authoritative and indispensible resource of its kind. In this classic text, prominent researchers in the field expertly survey the entire spectrum of neural science, giving an up-to-date, unparalleled view of the discipline for anyone who studies brain and mind. Here, in one remarkable volume, is the current state of neural science knowledge - ranging from molecules and cells, to anatomic structures and systems, to the senses and cognitive functions-all supported by more than 900 precise, full-color illustrations. In addition to clarifying complex topics, the book also benefits from a cohesive organization, beginning with an insightful overview of the interrelationships between the brain, nervous system, genes, and behavior. Principles of Neural Science then proceeds with an in-depth examination of the molecular and cellular biology of nerve cells, synaptic transmission, and the neural basis of cognition. The remaining sections illuminate how cells, molecules, and systems give us sight, hearing, touch, movement, thought, learning, memories, and emotions. The new fifth edition of Principles of Neural Science is thoroughly updated to reflect the tremendous amount of research, and the very latest clinical perspectives, that have significantly transformed the field within the last decade. Ultimately, Principles of Neural Science affirms that all behavior is an expression of neural activity, and that the future of clinical neurology and psychiatry hinges on the progress of neural science. Far exceeding the scope and scholarship of similar texts, this unmatched guide offers a commanding, scientifically rigorous perspective on the molecular mechanisms of neural function and disease-one that you'll continually rely on to advance your comprehension of brain, mind, and behavior. Features: the cornerstone reference in the field of neuroscience that explains how the nerves, brain, and mind function; clear emphasis on how behavior can be examined through the electrical activity of both individual neurons and systems of nerve cells; current focus on molecular biology as a tool for probing the pathogenesis of many neurological diseases, including muscular dystrophy, Huntington disease, and certain forms of Alzheimer's disease; more than 900 engaging full-color illustrations - including line drawings, radiographs, micrographs, and medical photographs clarify often-complex neuroscience concepts; outstanding section on the development and emergence of behavior, including important coverage of brain damage repair, the sexual differentiation of the nervous system, and the aging brain. Features: more detailed discussions of cognitive and behavioral functions, and an expanded review of cognitive processes; a focus on the increasing importance of computational neural science, which enhances our ability to record the brain's electrical activity and study cognitive processes more directly; and chapter-opening. Key concepts: provides a convenient, study-enhancing introduction to the material covered in each chapter; selected readings and full reference citations at the close of each chapter facilitate further study and research; and helpful appendices highlight basic circuit theory; the neurological examination of the patient; circulation of the brain; the blood-brain barrier, choroid plexus, and cerebrospinal fluid; neural networks; and theoretical approaches to neuroscience.

An Introduction to Programming by the Inventor of C++
Preparation for Programming in the Real World
The book assumes that you aim eventually to write non-trivial programs, whether for work in software development or in some other technical field.
Focus on Fundamental Concepts and Techniques
The book explains fundamental concepts and techniques in greater depth than traditional introductions. This approach will give you a solid foundation for writing useful, correct, maintainable, and efficient code.
Programming with Today’s C++ (C++11 and C++14)
The book is an introduction to programming in general, including object-oriented programming and generic programming. It is also a solid introduction to the C++ programming language, one of the most widely used languages for real-world software. The book presents modern C++ programming techniques from the start,
introducing the C++ standard library and C++11 and C++14 features to simplify programming tasks.
For Beginners—And Anyone Who Wants to Learn Something New
The book is primarily designed for people who have never programmed before, and it has been tested with many thousands of first-year university students. It has also been extensively used for self-study. Also, practitioners and advanced students have gained new insight and guidance by seeing how a master approaches the elements of his art.
Provides a Broad View
The first half of the book covers a wide range of essential concepts, design and programming techniques, language features, and libraries. Those will enable you to write programs involving input, output, computation, and simple graphics. The second half explores more specialized topics (such as text processing, testing, and the C programming language) and provides abundant reference material. Source code and support supplements are available from the author’s website.

C+11 has arrived: thoroughly master it, with the definitive new guide from C++ creator Bjarne Stroustrup, C++ Programming Language, Fou rth Edition! The brand-new edition of the world's most trusted and widely read guide to C++, it has been comprehensively updated for the long-awaited C++11 standard. Extensively rewritten to present the C++11 language, standard library, and key design techniques as an integrated whole, Stroustrup thoroughly addresses changes that make C++11 feel like a whole new language, offering definitive guidance for leveraging its improvements in performance, reliability, and clarity. C++ programmers around the world recognize Bjarne Stoustrup as the go-to expert for the absolutely authoritative and exceptionally useful information they need to write outstanding C++ programs. Now, as C++11 compilers arrive and development organizations migrate to the new standard, they know exactly where to turn once more: Stoustrup's C++ Programming Language, Fourth Edition.

TensorFlow, a popular library for machine learning, embraces the innovation and community-engagement of open source, but has the support, guidance, and stability of a large corporation. Because of its multitude of strengths, TensorFlow is appropriate for individuals and businesses ranging from startups to companies as large as, well, Google. TensorFlow is currently being used for natural language processing, artificial intelligence, computer vision, and predictive analytics. TensorFlow, open sourced to the public by Google in November 2015, was made to be flexible, efficient, extensible, and portable. Computers of any shape and size can run it, from smartphones all the way up to huge computing clusters. This book is for anyone who knows a little machine learning (or not) and who has heard about TensorFlow, but found the documentation too daunting to approach. It introduces the TensorFlow framework and the underlying machine learning concepts that are important to harness machine intelligence. After reading this book, you should have a deep understanding of the core TensorFlow API.

This is a book about how we see: the environment around us (its surfaces, their layout, and their colors and textures); where we are in the environment; whether or not we are moving and, if we are, where we are going; what things are good for; how to do things (to thread a needle or drive an automobile); or why things look as they do. The basic assumption is that vision depends on the eye which is connected to the brain. The author suggests that natural vision depends on the eyes in the head on a body supported by the ground, the brain being only the central organ of a complete visual system. When no constraints are put on the visual system, people look around, walk up to something interesting and move around it so as to see it from all sides, and go from one vista to another. That is natural vision -- and what this book is about.