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.

    Stewart's Calculus is successful throughout the world because he explains the material in a way that makes sense to a wide variety of readers. His explanations make ideas come alive, and his problems challenge, to reveal the beauty of calculus. Stewart's examples stand out because they are not just models for problem solving or a means of demonstrating techniques--they also encourage readers to develp an analytic view of the subject. This edition includes new problems, examples, and projects.

    We are living in an age where data comes in abundance, and thanks to the self-learning algorithms from the field of machine learning, we can turn this data into knowledge. Automated speech recognition on our smart phones, web search engines, e-mail spam filters, the recommendation systems of our favorite movie streaming services – machine learning makes it all possible.Thanks to the many powerful open-source libraries that have been developed in recent years, machine learning is now right at our fingertips. Python provides the perfect environment to build machine learning systems productively.This book will teach you the fundamentals of machine learning and how to utilize these in real-world applications using Python. Step-by-step, you will expand your skill set with the best practices for transforming raw data into useful information, developing learning algorithms efficiently, and evaluating results.You will discover the different problem categories that machine learning can solve and explore how to classify objects, predict continuous outcomes with regression analysis, and find hidden structures in data via clustering. You will build your own machine learning system for sentiment analysis and finally, learn how to embed your model into a web app to share with the world

    On Lisp explains the reasons behind Lisp's growing popularity as a mainstream programming language. On Lisp is a comprehensive study of advanced Lisp techniques, with bottom-up programming as the unifying theme. It gives the first complete description of macros and macro applications. The book also covers important subjects related to bottom-up programming, including functional programming, rapid prototyping, interactive development, and embedded languages. The final chapter takes a deeper look at object-oriented programming than previous Lisp books, showing the step-by-step construction of a working model of the Common Lisp Object System (CLOS). As well as an indispensable reference, On Lisp is a source of software. Its examples form a library of functions and macros that readers will be able to use in their own Lisp programs.

    It follows standards-based principles, but also teaches readers ways around problems they are likely to face using (X)HTML.While XHTML is the "current" standard, the book still covers HTML because many people do not yet understand that XHTML is the official successor to HTML, and many readers will still stick with HTML for backward compatibility and simpler/informal Web pages that don't require XHTML compliance.The book teaches basic principles of usability and accessibility along the way, to get users into the mode of developing Web pages that will be available to as many viewers as possible from the start. The book also covers the most commonly used programming/scripting language -- JavaScript -- and provides readers with a roadmap of other Web technologies to learn after mastering this book to add more functionality to their sites.

    Yet for all the talk about the democratizing power of the Internet, regimes in Iran and China are as stable and repressive as ever. In fact, authoritarian governments are effectively using the Internet to suppress free speech, hone their surveillance techniques, disseminate cutting-edge propaganda, and pacify their populations with digital entertainment. Could the recent Western obsession with promoting democracy by digital means backfire?In this spirited book, journalist and social commentator Evgeny Morozov shows that by falling for the supposedly democratizing nature of the Internet, Western do-gooders may have missed how it also entrenches dictators, threatens dissidents, and makes it harder - not easier - to promote democracy. Buzzwords like "21st-century statecraft" sound good in PowerPoint presentations, but the reality is that "digital diplomacy" requires just as much oversight and consideration as any other kind of diplomacy.Marshaling compelling evidence, Morozov shows why we must stop thinking of the Internet and social media as inherently liberating and why ambitious and seemingly noble initiatives like the promotion of "Internet freedom" might have disastrous implications for the future of democracy as a whole.

    With a logical, body-systems organization and engaging terminology exercises throughout, it's your key to communicating confidently and effectively with other health care professionals.Systematic approach to terminology prepares you to recognize and define new words as you encounter them and build the medical vocabulary you'll need in the health care setting.Pronunciation key provides quick access to frequently referenced material.Complimentary and Alternative Medicine terms boxes highlight words and phrases associated with this increasingly popular discipline.Case studies encourage critical thinking and demonstrate how to apply the information you've learned.Terminology flash cards, included with every book, give you valuable review and self-assessment tools you can take anywhere for study on the go.Evolve resources enhance your learning and reinforcement opportunities with additional exercises, a Spanish/English glossary, and the Body Spectrum Electronic Anatomy Coloring Book.Medical Terminology Online, available at an additional charge, gives you access to a complete online course for the most advanced learning and understanding.New terms and abbreviations familiarize you with the latest terminology in use in health care.New images and illustrations visually acquaint you with pathologic information and procedures you'll encounter in the clinical setting.Enhanced chapter features highlight important concepts and provide guidance for more effective learning and study.CD references within the text direct you to expanded learning opportunities on the companion CD.More than 20 new medical records let you practice medical terminology using the forms you'll encounter in the clinical setting.New icons make it easy to distinguish a variety of helpful boxes and reference the material you need quickly.Answers to review exercises help you gauge your strengths and weaknesses and configure the most effective study plan for you.Website boxes refer you to valuable content you can access online for further learning.Revised pharmacy appendix helps you easily reference key pharmaceutical terms.The vastly updated companion CD provides fun alternatives for reinforcing what you've learned with new learning games, including Medical Millionaire and Termbusters.Enhanced audio companion, available on CD or as iTerms downloads for portable media players, helps you perfect your pronunciation skills and confidently use the terms you've learned in practice.

    Revised with three new sermons, inclusive language, and NRSV texts, it is still as fresh and provocative as ever.

    Using Scala, you can write programs more concisely than in Java, as well as leverage the full power of concurrency. Since Scala runs on the JVM, it can access any Java library and is interoperable with Java frameworks. Scala for the Impatient concisely shows developers what Scala can do and how to do it. In this book, Cay Horstmann, the principal author of the international best-selling Core Java(TM), offers a rapid, code-based introduction that's completely practical. Horstmann introduces Scala concepts and techniques in "blog-sized" chunks that you can quickly master and apply. Hands-on activities guide you through well-defined stages of competency, from basic to expert. Coverage includes Getting started quickly with Scala's interpreter, syntax, tools, and unique idioms Mastering core language features: functions, arrays, maps, tuples, packages, imports, exception handling, and more Becoming familiar with object-oriented programming in Scala: classes, inheritance, and traits Using Scala for real-world programming tasks: working with files, regular expressions, and XML Working with higher-order functions and the powerful Scala collections library Leveraging Scala's powerful pattern matching and case classes Creating concurrent programs with Scala actors Implementing domain-specific languages Understanding the Scala type system Applying advanced "power tools" such as annotations, implicits, and delimited continuations Scala is rapidly reaching a tipping point that will reshape the experience of programming. This book will help object-oriented programmers build on their existing skills, allowing them to immediately construct useful applications as they gradually master advanced programming techniques.

    You'll also learn how to make your programs interactive and how to test your code safely before adding it to a project. In the second half of the book, you'll put your new knowledge into practice with three substantial projects: a Space Invaders-inspired arcade game, data visualizations with Python's super-handy libraries, and a simple web app you can deploy online.As you work through Python Crash Course, you'll learn how to: Use powerful Python libraries and tools, including matplotlib, NumPy, and PygalMake 2D games that respond to keypresses and mouse clicks, and that grow more difficult as the game progressesWork with data to generate interactive visualizationsCreate and customize simple web apps and deploy them safely onlineDeal with mistakes and errors so you can solve your own programming problemsIf you've been thinking seriously about digging into programming, Python Crash Course will get you up to speed and have you writing real programs fast. Why wait any longer? Start your engines and code!

    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.

    In The Age of Intelligent Machines, inventor and computer scientist Raymond Kurzweil probes the past, present, and future of artificial intelligence, from its earliest philosophical and mathematical roots to tantalizing glimpses of 21st-century machines with superior intelligence and prodigious speed and memory.

    Most developers have a limited understanding of compositional techniques. It's time for that to change.In "Composing Software", Eric Elliott shares the fundamentals of composition, including both function composition and object composition, and explores them in the context of JavaScript. The book covers the foundations of both functional programming and object oriented programming to help the reader better understand how to build and structure complex applications using simple building blocks.You'll learn: • Functional programming • Object composition • How to work with composite data structures • Closures • Higher order functions • Functors (e.g., array.map) • Monads (e.g., promises) • Transducers • LensesAll of this in the context of JavaScript, the most used programming language in the world. But the learning doesn't stop at JavaScript. You'll be able to apply these lessons to any language. This book is about the timeless principles of software composition and its lessons will outlast the hot languages and frameworks of today. Unlike most programming books, this one may still be relevant 20 years from now.This book began life as a popular blog post series that attracted hundreds of thousands of readers and influenced the way software is built at many high growth tech startups and fortune 500 companies.

    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

    With it has come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting—the first comprehensive treatment of this topic in any book. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie wrote much of the statistical modeling software in S-PLUS and invented principal curves and surfaces. Tibshirani proposed the Lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, and projection pursuit.