Linear algebra is tightly integrated into the text.

    This 12-hour free course examined the Holocaust, historical arguments surrounding it, whether it is unique and why it happened as and when it did.

     Neural networks are a key element of deep learning and artificial intelligence, which today is capable of some truly impressive feats. Yet too few really understand how neural networks actually work. This guide will take you on a fun and unhurried journey, starting from very simple ideas, and gradually building up an understanding of how neural networks work. You won't need any mathematics beyond secondary school, and an accessible introduction to calculus is also included. The ambition of this guide is to make neural networks as accessible as possible to as many readers as possible - there are enough texts for advanced readers already! You'll learn to code in Python and make your own neural network, teaching it to recognise human handwritten numbers, and performing as well as professionally developed networks. Part 1 is about ideas. We introduce the mathematical ideas underlying the neural networks, gently with lots of illustrations and examples. Part 2 is practical. We introduce the popular and easy to learn Python programming language, and gradually builds up a neural network which can learn to recognise human handwritten numbers, easily getting it to perform as well as networks made by professionals. Part 3 extends these ideas further. We push the performance of our neural network to an industry leading 98% using only simple ideas and code, test the network on your own handwriting, take a privileged peek inside the mysterious mind of a neural network, and even get it all working on a Raspberry Pi. All the code in this has been tested to work on a Raspberry Pi Zero.

    In addition to being the most up-to-date communications text available, Simon Haykin has added MATLAB computer experiments.

    However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation. Similarly, new models based on kernels have had a significant impact on both algorithms and applications. This new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners, and assumes no previous knowledge of pattern recognition or machine learning concepts. Knowledge of multivariate calculus and basic linear algebra is required, and some familiarity with probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

    Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications.Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability.The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.

    It covers the basic steps needed to create a vSphere HA and vSphere DRS cluster and to implement vSphere Storage DRS. Even more important, it explains the concepts and mechanisms behind HA, DRS and Storage DRS which will enable you to make well educated decisions. This book will take you in to the trenches of HA, DRS and Storage DRS and will give you the tools to understand and implement e.g. HA admission control policies, DRS resource pools, Datastore Clusters and resource allocation settings. On top of that each section contains basic design principles that can be used for designing, implementing or improving VMware infrastructures and fundamental supporting features like (Storage) vMotion, Storage I/O Control and much more are described in detail for the very first time. This book is also the ultimate guide to be prepared for any HA, DRS or Storage DRS related question or case study that might be presented during VMware VCDX, VCP and or VCAP exams.Coverage includes: HA node types HA isolation detection and response HA admission control VM Monitoring HA and DRS integration DRS imbalance algorithm Resource Pools Impact of reservations and limits CPU Resource Scheduling Memory Scheduler DPM Datastore Clusters Storage DRS algorithm Influencing SDRS recommendationsBe prepared to dive deep!

    KEY TOPICS: This classic book enables readers to make connections between classical and modern physics - an indispensable part of a physicist's education. In this new edition, Beams Medal winner Charles Poole and John Safko have updated the book to include the latest topics, applications, and notation, to reflect today's physics curriculum. They introduce readers to the increasingly important role that nonlinearities play in contemporary applications of classical mechanics. New numerical exercises help readers to develop skills in how to use computer techniques to solve problems in physics. Mathematical techniques are presented in detail so that the book remains fully accessible to readers who have not had an intermediate course in classical mechanics. MARKET: For college instructors and students.

    Thoroughly updated to include Java 6, the Third Edition of Horstmann's bestselling text helps you absorb computing concepts and programming principles, develop strong problem-solving skills, and become a better programmer, all while exploring the elements of Java that are needed to write real-life programs. A top-notch introductory text for beginners, Big Java, Third Edition is also a thorough reference for students and professionals alike to Java technologies, Internet programming, database access, and many other areas of computer science.Features of the Third Edition: The 'Objects Gradual' approach leads you into object-oriented thinking step-by-step, from using classes, implementing simple methods, all the way to designing your own object-oriented programs. A strong emphasis on test-driven development encourages you to consider outcomes as you write programming code so you design better, more usable programs Helpful "Testing Track" introduces techniques and tools step by step, ensuring that you master one before moving on to the next New teaching and learning tools in WileyPLUS--including a unique assignment checker that enables you to test your programming problems online before you submit them for a grade Graphics topics are developed gradually throughout the text, conveniently highlighted in separate color-coded sections Updated coverage is fully compatible with Java 5 and includes a discussion of the latest Java 6 features

    Short chapters are paired with free interactive online exercises to teach the fundamentals of HTML and CSS. Written for beginners, useful for experienced developers who want to sharpen their skills. Prepares the reader to code a website of medium complexity. The learner spends two to three times as long practicing as he does reading. Based on cognitive research showing that retention increases 400 percent when learners are challenged to retrieve the information they just read. Explanations are in plain, nontechnical English that people of all backgrounds can readily understand. With ample coding examples and illustrations.

    It provides a simple mathematical framework which can be used to understand and even design biological circuits. The textavoids specialist terms, focusing instead on several well-studied biological systems that concisely demonstrate key principles. An Introduction to Systems Biology: Design Principles of Biological Circuits builds a solid foundation for the intuitive understanding of general principles. It encourages the reader to ask why a system is designed in a particular way and then proceeds to answer with simplified models.

    Pure Mathematics 1 corresponds to unit P1. It covers quadratics, functions, coordinate geometry, circular measure, trigonometry, vectors, series, differentiation and integration.

    It explores the services that banks and their principal competitors (including savings and loans, credit unions, security and investment firms) offer in an increasingly competitive financial-services marketplace. The ninth edition discusses the major changes and events that are remaking banking and financial services today. Among the key events and unfolding trends covered in the text are: Newest Reforms in the Financial System, including the new Dodd-Frank Financial Reform Law and the Credit Card Accountability, Responsibility, and Disclosure (CARD) Act of 2009. Global Financial Sector coverage of the causes and impact of the latest "great recession." Systemic Risk and the presentation of the challenges posed in the financial system. Exploration of changing views on the "too big to fail" (TBTF) doctrine and how regulators may be forced to deal with TBTF in the future. Controlling Risk Exposure presentation of methods in an increasingly volatile economy

     FreeMind is a premier mind mapping software written in Java. It is a high-productivity tool that can make all your online mind mapping simple. Organize, prioritize, know where you are, where you've been and where you're heading with FreeMind. Mind mapping can be used for brainstorming, goal planning, product design, event planning, and so much more - the only limit is your own creativity. Use this guide's step-by-step instructions and screenshots to learn how to create your own digital mind maps. THIS BOOK DOES NOT TEACH YOU MIND MAPPING CONCEPTS OR BASICS. It ONLY shows you how to use the FreeMind software that creates digital mind maps. If you are new to mind maps, you may want to pick up Mind Mapping Secrets - Achieving Your Goals for a quick primer on how to create mind maps using pen and paper. Then take your maps to a new level with this easy to master How To guide today.

    It provides students with skills that will enable them to make productive use of computational techniques, including some of the tools and techniques of "data science" for using computation to model and interpret data. The book is based on an MIT course (which became the most popular course offered through MIT's OpenCourseWare) and was developed for use not only in a conventional classroom but in in a massive open online course (or MOOC) offered by the pioneering MIT--Harvard collaboration edX.Students are introduced to Python and the basics of programming in the context of such computational concepts and techniques as exhaustive enumeration, bisection search, and efficient approximation algorithms. The book does not require knowledge of mathematics beyond high school algebra, but does assume that readers are comfortable with rigorous thinking and not intimidated by mathematical concepts. Although it covers such traditional topics as computational complexity and simple algorithms, the book focuses on a wide range of topics not found in most introductory texts, including information visualization, simulations to model randomness, computational techniques to understand data, and statistical techniques that inform (and misinform) as well as two related but relatively advanced topics: optimization problems and dynamic programming.Introduction to Computation and Programming Using Python can serve as a stepping-stone to more advanced computer science courses, or as a basic grounding in computational problem solving for students in other disciplines.