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.

    The novel approach taken here banishes determinants to the end of the book and focuses on the central goal of linear algebra: understanding the structure of linear operators on vector spaces. The author has taken unusual care to motivate concepts and to simplify proofs. For example, the book presents - without having defined determinants - a clean proof that every linear operator on a finite-dimensional complex vector space (or an odd-dimensional real vector space) has an eigenvalue. A variety of interesting exercises in each chapter helps students understand and manipulate the objects of linear algebra. This second edition includes a new section on orthogonal projections and minimization problems. The sections on self-adjoint operators, normal operators, and the spectral theorem have been rewritten. New examples and new exercises have been added, several proofs have been simplified, and hundreds of minor improvements have been made throughout the text.

    Each chapter gives you the complete source code for a new game and teaches the programming concepts from these examples. The book is available under a Creative Commons license and can be downloaded in full for free from http: //inventwithpython.com/pygame This book was written to be understandable by kids as young as 10 to 12 years old, although it is great for anyone of any age who has some familiarity with Python.

    Leading networking authority Douglas Comer presents a wide-ranging, self-contained tour of the concepts, principles, and technologies that enable today's Internet to support applications ranging from web browsing to telephony and multimedia. This Fifth Edition has been thoroughly reorganized, revised, and updated: it includes extensive new coverage of topics ranging from wireless protocols to network performance, while reducing or eliminating coverage of older protocols and technologies. Comer begins by illuminating the applications and facilities offered by today's Internet. Next, he systematically introduces the underlying network technologies and protocols that make them possible: low-level data communications; packet switching, LAN, and WAN technologies; and Internet protocols such as TCP, IP, UDP, and IPv6. With these concepts and technologies established, he introduces several of the most important contemporary issues faced by network implementers and managers, including quality of service, Internet telephony, multimedia, network security, and network management. Comer has carefully designed this book to support both top-down and bottom-up teaching approaches. Students need no background in operating systems, and no sophisticated math: Comer relies throughout on figures, drawings, examples, and analogies, "not" mathematical proofs.

    R in a Nutshell provides a quick and practical way to learn this increasingly popular open source language and environment. You'll not only learn how to program in R, but also how to find the right user-contributed R packages for statistical modeling, visualization, and bioinformatics.The author introduces you to the R environment, including the R graphical user interface and console, and takes you through the fundamentals of the object-oriented R language. Then, through a variety of practical examples from medicine, business, and sports, you'll learn how you can use this remarkable tool to solve your own data analysis problems.Understand the basics of the language, including the nature of R objectsLearn how to write R functions and build your own packagesWork with data through visualization, statistical analysis, and other methodsExplore the wealth of packages contributed by the R communityBecome familiar with the lattice graphics package for high-level data visualizationLearn about bioinformatics packages provided by Bioconductor"I am excited about this book. R in a Nutshell is a great introduction to R, as well as a comprehensive reference for using R in data analytics and visualization. Adler provides 'real world' examples, practical advice, and scripts, making it accessible to anyone working with data, not just professional statisticians."

    On any given Saturday, in dozens of stadiums across America, you will find crowds in excess of 75,000 gathered to root on their teams. This book is their Bible???a rich and comprehensive reference guide to the game??'s history, tradition and lore. Based on three years of research by the nation??'s foremost football experts, the book features: ???? ???? ??Capsule histories for each of the 119 Division 1-A programs, the Ivy League schools and teams from the SWAC, MEAC and historically black colleges ??????????????Year-by-year schedules and records ??????????????Statistical leaders from every school ??????????????Fightsong lyrics ??????????????Box scores for every bowl game ever played ??????????????4-color insert illustrating the evolution of each school??'s helmet design ??????????????Weekly polls dating back to 1936 ??????????????Essays by the game??'s top wordsmiths (Dan Jenkins, Beano Cook, Chris Fowler, Gene Wojciechowski) ??????????????Plus a lively round table discussion with ESPN??'s popular Game Day Team (Fowler, LeeCorso and Kirk Herbstreit) Packed with tables and charts and designed in an easy-to-read style, the updated ESPN College Football Encyclopedia will continue to dazzle even the most knowledgeable fan.

    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

    For the first time, these books are available as a boxed, three-volume set. The handsome slipcase makes this set an ideal gift for the recent computer science graduate or professional programmer. Offering a description of classical computer science, this multi-volume work is a useful resource in programming theory and practice for students, researchers, and practitioners alike. For programmers, it offers cookbook solutions to their day-to-day problems.

    More than 70,000 rhyming words. Includes multi-word rhymes. Fully cross-referenced for ease of use. Based on the best-selling Merriam-Webster’s Collegiate® Dictionary.

    Stat Shot is a fun and informative guide hockey fans can use to understand and enjoy what analytics says about team building, a player’s junior numbers, measuring faceoff success, recording save percentage, the most one-sided trades in history, and everything you ever wanted to know about shot-based metrics. Acting as an invaluable supplement to traditional analysis, Stat Shot can be used to test the validity of conventional wisdom, and to gain insight into what teams are doing behind the scenes — or maybe what they should be doing.Whether looking for a reference for leading-edge research and hard-to-find statistical data, or for passionate and engaging storytelling, Stat Shot belongs on every serious hockey fan’s bookshelf.

    Bill James has been called “baseball’s shrewdest analyst” (Slate) and “part of baseball legend” (The New Yorker), and his Baseball Abstract has been acclaimed as the “holy book of baseball” (Chicago Tribune). Thirty years ago, James introduced a new approach to evaluating players and strategies, and now his theories have become indispensable tools for agents, statistics analysts, maverick general managers, and anyone who is serious about understanding the game.James began writing about baseball while working at a factory in his native Kansas. In lively, often acerbic prose, he used statistics to challenge entrenched beliefs and uncover surprising truths about the game. His annual Baseball Abstract captured the attention of fans and front offices and went on to become a bestselling staple of the baseball book category. In 2002, the Boston Red Sox hired James as an advisor. Two years later they achieved their long-awaited World Series triumph.The Mind of Bill James tells the story of how a gifted outsider inspired a new understanding of baseball. It delves deeply into James’s essential wisdom–including his surprising beliefs about pitch counts and the importance of batting-order, thoughts on professionalism and psychology, and why teams tend to develop the characteristics that are least favored by their home parks. It also brings together his best writing, much of it long out of print, as well as insights from new interviews. Written with James’ full cooperation, it is at once an eye-opening portrait of baseball’s virtuoso analyst and a treasury of his idiosyncratic genius.

    Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package—PMTK (probabilistic modeling toolkit)—that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

    During that week, you will learn almost everything modern machine learning has to offer. The author and other practitioners have spent years learning these concepts.Companion wiki — the book has a continuously updated wiki that extends some book chapters with additional information: Q&A, code snippets, further reading, tools, and other relevant resources.Flexible price and formats — choose from a variety of formats and price options: Kindle, hardcover, paperback, EPUB, PDF. If you buy an EPUB or a PDF, you decide the price you pay!Read first, buy later — download book chapters for free, read them and share with your friends and colleagues. Only if you liked the book or found it useful in your work, study or business, then buy it.

    It takes a fairly traditional approach, emphasizing problem solving, design, and programming as the core skills of computer science. However, these ideas are illustrated using a non-traditional language, namely Python. Although I use Python as the language, teaching Python is not the main point of this book. Rather, Python is used to illustrate fundamental principles of design and programming that apply in any language or computing environment. In some places, I have purposely avoided certain Python features and idioms that are not generally found in other languages. There are already many good books about Python on the market; this book is intended as an introduction to computing. Features include the following: *Extensive use of computer graphics. *Interesting examples. *Readable prose. *Flexible spiral coverage. *Just-in-time object coverage. *Extensive end-of-chapter problems.

    Algorithms in a Nutshell describes a large number of existing algorithms for solving a variety of problems, and helps you select and implement the right algorithm for your needs -- with just enough math to let you understand and analyze algorithm performance. With its focus on application, rather than theory, this book provides efficient code solutions in several programming languages that you can easily adapt to a specific project. Each major algorithm is presented in the style of a design pattern that includes information to help you understand why and when the algorithm is appropriate. With this book, you will:Solve a particular coding problem or improve on the performance of an existing solutionQuickly locate algorithms that relate to the problems you want to solve, and determine why a particular algorithm is the right one to useGet algorithmic solutions in C, C++, Java, and Ruby with implementation tipsLearn the expected performance of an algorithm, and the conditions it needs to perform at its bestDiscover the impact that similar design decisions have on different algorithmsLearn advanced data structures to improve the efficiency of algorithmsWith Algorithms in a Nutshell, you'll learn how to improve the performance of key algorithms essential for the success of your software applications.