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.

    The algorithms you'll use most often as a programmer have already been discovered, tested, and proven. If you want to take a hard pass on Knuth's brilliant but impenetrable theories and the dense multi-page proofs you'll find in most textbooks, this is the book for you. This fully-illustrated and engaging guide makes it easy for you to learn how to use algorithms effectively in your own programs.Grokking Algorithms is a disarming take on a core computer science topic. In it, you'll learn how to apply common algorithms to the practical problems you face in day-to-day life as a programmer. You'll start with problems like sorting and searching. As you build up your skills in thinking algorithmically, you'll tackle more complex concerns such as data compression or artificial intelligence. Whether you're writing business software, video games, mobile apps, or system utilities, you'll learn algorithmic techniques for solving problems that you thought were out of your grasp. For example, you'll be able to:Write a spell checker using graph algorithmsUnderstand how data compression works using Huffman codingIdentify problems that take too long to solve with naive algorithms, and attack them with algorithms that give you an approximate answer insteadEach carefully-presented example includes helpful diagrams and fully-annotated code samples in Python. By the end of this book, you will know some of the most widely applicable algorithms as well as how and when to use them.

    Aided by a number of stunning illustrations, David Billington discusses leading structural engineer-artists, such as John A. Roebling, Gustave Eiffel, Fazlur Khan, and Robert Maillart.

    

    The concepts and techniques described are the actual ones used by real game studios like Electronic Arts and Naughty Dog. The examples are often grounded in specific technologies, but the discussion extends way beyond any particular engine or API. The references and citations make it a great jumping off point for those who wish to dig deeper into any particular aspect of the game development process.Intended as the text for a college level series in game programming, this book can also be used by amateur software engineers, hobbyists, self-taught game programmers, and existing members of the game industry. Junior game engineers can use it to solidify their understanding of game technology and engine architecture. Even senior engineers who specialize in one particular field of game development can benefit from the bigger picture presented in these pages.

    He knows that if parents place good video games into a learning system in their homes they can reap major benefits for their children and themselves. They can accelerate their children's language and cognitive growth." —James Paul Gee, Tashia Mogridge Professor of Reading, University of Wisconsin-MadisonMarc Prensky presents the case—profoundly counter-cultural but true nevertheless—that video and computer game playing, within limits, is actually very beneficial to today's "Digital Native" kids, who are using them to prepare themselves for life in the 21st century. The reason kids are so attracted to these games, Prensky says, is that they are learning about important "future" things, from collaboration, to prudent risk taking, to strategy formulation and execution, to complex moral and ethical decisions. Prensky's arguments are backed up by university PhD's studying not just violence, but games in their totality, as well as studies of gamers who have become successful corporate workers, entrepreneurs, leaders, doctors, lawyers, scientists and other professionals.Because most adults (including the critics) can't play the modern complex games themselves (and discount the opinions of the kids who do play them) they rely on secondhand sources of information, most of whom are sadly misinformed about both the putative harm and the true benefits of game-playing. This book is the antidote to those misinformed, bombastic sources, in the press and elsewhere. Full of common sense and practical information, it provides parents with a large number of techniques approaches they can use—both over time and right away—to improve both their understanding of games and their relationships with their kids. What You Will Learn The aim of this book is to give you a peek into the hidden world into which your kids disappear when they are playing games, and to help you as an adult—especially if you are a concerned parent or teacher—understand and appreciate just how much your kids are learning that is POSITIVE from their video and computer games.In the few short hours it takes to read this book, you will learn: What it feels like to be in the world of computer and video games; How to appreciate the breadth and depth of modern computer and video games and the ways they make your kids learn; How to understand the various USEFUL skills your game-playing your kids are acquiring; How to understand your own kids better and build better relationships using games as a base; And, most importantly, How to augment and improve what your kids are learning by HAVING CONVERSATIONS THAT THEY WANT TO HAVE about their games.

    Based on B. P. Lathi's widely used book, Linear Systems and Signals, it features additional applications to communications, controls, and filtering as well as new chapters on analog and digital filters and digital signal processing. Lathi emphasizes the physical appreciation of concepts rather than the mere mathematical manipulation of symbols. Avoiding the tendency to treat engineering as a branch of applied mathematics, he uses mathematics to enhance physical and intuitive understanding of concepts, instead of employing it only to prove axiomatic theory. Theoretical results are supported by carefully chosen examples and analogies, allowing students to intuitively discover meaning for themselves.

    The book offers clear instruction and real-world examples from training expert and bestselling author Mike Meyers along with hundreds of accurate practice questions.Fulfilling the promise of the All-in-One series, this complete reference serves both as a study tool and a valuable on-the-job reference that will serve readers beyond the exam. CompTIA Network+ Certification All-in-One Exam Guide, Seventh Edition (Exam N10-007) also includes access to free video training and interactive hands-on labs and simulations that prepare you for difficult performance-based questions. A valuable pre-assessment test enables readers to gauge their familiarity with the test’s objectives and tailor an effective course for study.-Contains complete coverage of every objective for the CompTIA Network+ Certification exam-Written by CompTIA training and certification guru Mike Meyers- Electronic content includes the Total Tester exam simulator with over 100 practice questions, over an hour of training videos, and a collection of Mike Meyers’ favorite shareware and freeware networking utilities

    Intended for developers who are already familiar with the Apex language, and experienced Java and C# developers who are moving to Apex, this book starts where the Force.com documentation leaves off. Instead of trying to cover all of the features of the platform, Advanced Apex programming focuses entirely on the Apex language and core design patterns. You’ll learn how to truly think in Apex – to embrace limits and bulk patterns. You’ll see how to develop architectures for efficient and reliable trigger handling, and for asynchronous operations. You’ll discover that best practices differ radically depending on whether you are building software for a specific organization or for a managed package. And you’ll find approaches for incorporating testing and diagnostic code that can dramatically improve the reliability and deployment of Apex software, and reduce your lifecycle and support costs. Based on his experience both as a consultant and as architect of a major AppExchange package, Dan Appleman focuses on the real-world problems and issues that are faced by Apex developers every day, along with the obscure problems and surprises that can sneak up on you if you are unprepared.

    Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, recognize faces or spoken speech, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. "Introduction to Machine Learning" is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. It discusses many methods based in different fields, including statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, and data mining, in order to present a unified treatment of machine learning problems and solutions. All learning algorithms are explained so that the student can easily move from the equations in the book to a computer program. The book can be used by advanced undergraduates and graduate students who have completed courses in computer programming, probability, calculus, and linear algebra. It will also be of interest to engineers in the field who are concerned with the application of machine learning methods.After an introduction that defines machine learning and gives examples of machine learning applications, the book covers supervised learning, Bayesian decision theory, parametric methods, multivariate methods, dimensionality reduction, clustering, nonparametric methods, decision trees, linear discrimination, multilayer perceptrons, local models, hidden Markov models, assessing and comparing classification algorithms, combining multiple learners, and reinforcement learning.

    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

    Commercial game development expert Robert Nystrom presents an array of general solutions to problems encountered in game development. For example, you'll learn how double-buffering enables a player to perceive smooth and realistic motion, and how the service locator pattern can help you provide access to services such as sound without coupling your code to any particular sound driver or sound hardware. Games have much in common with other software, but also a number of unique constraints. Some of the patterns in this book are well-known in other domains of software development. Other of the patterns are unique to gaming. In either case, Robert Nystrom bridges from the ivory tower world of software architecture to the in-the-trenches reality of hardcore game programming. You'll learn the patterns and the general problems that they solve. You'll come away able to apply powerful and reusable architectural solutions that enable you to produce higher quality games with less effort than before. Applies classic design patterns to game programming. Introduces new patterns specific to game programming. Brings abstract software architecture down to Earth with approachable writing and an emphasis on simple code that shows each pattern in practice. What you'll learn Overcome architectural challenges unique to game programming Apply lessons from the larger software world to games. Tie different parts of a game (graphics, sound, AI) into a cohesive whole. Create elegant and maintainable architecture. Achieve good, low-level performance. Gain insight into professional, game development. Who this book is forGame Programming Patterns is aimed at professional game programmers who, while successful in shipping games, are frustrated at how hard it sometimes is to add and modify features when a game is under development. Game Programming Patterns shows how to apply modern software practices to the problem of game development while still maintaining the blazing-fast performance demanded by hard-core gamers. Game Programming Patterns also appeals to those learning about game programming in their spare time. Hobbyists and aspiring professionals alike will find much to learn in this book about pathfinding, collision detection, and other game-programming problem domains.

    Readers learn how to reduce time constraints and develop programs efficiently by analyzing the feasibility of an algorithm before it is coded. The C++ language is brought up-to-date and simplified, and the Standard Template Library is now fully incorporated throughout the text. This Third Edition also features significantly revised coverage of lists, stacks, queues, and trees and an entire chapter dedicated to amortized analysis and advanced data structures such as the Fibonacci heap. Known for its clear and friendly writing style, Data Structures and Algorithm Analysis in C++ is logically organized to cover advanced data structures topics from binary heaps to sorting to NP-completeness. Figures and examples illustrating successive stages of algorithms contribute to Weiss' careful, rigorous and in-depth analysis of each type of algorithm.

    This long-awaited revision contains changes throughout the text. There are new implementations of most of the major programming systems in the book, including the interpreters and compilers, and the authors have incorporated many small changes that reflect their experience teaching the course at MIT since the first edition was published. A new theme has been introduced that emphasizes the central role played by different approaches to dealing with time in computational models: objects with state, concurrent programming, functional programming and lazy evaluation, and nondeterministic programming. There are new example sections on higher-order procedures in graphics and on applications of stream processing in numerical programming, and many new exercises. In addition, all the programs have been reworked to run in any Scheme implementation that adheres to the IEEE standard.

    Feynman gave his famous course on computation at the California Institute of Technology, he asked Tony Hey to adapt his lecture notes into a book. Although led by Feynman, the course also featured, as occasional guest speakers, some of the most brilliant men in science at that time, including Marvin Minsky, Charles Bennett, and John Hopfield. Although the lectures are now thirteen years old, most of the material is timeless and presents a “Feynmanesque” overview of many standard and some not-so-standard topics in computer science such as reversible logic gates and quantum computers.