In the successful, accessible style of Tony Gaddis’ best-selling texts, useful examples and detail-oriented explanations allow students to become comfortable with fundamental concepts and logical thought processes used in programming without the complication of language syntax. Students gain confidence in their program design skills to transition into more comprehensive programming courses.The book is ideal for a programming logic course taught as a precursor to a language-specific introductory programming course, or for the first part of an introductory programming course.

    Courses tailored to fit individual pupils. Textbooks that talk back. This is tomorrow’s education landscape, thanks to the power of big data. These advances go beyond the much-discussed rise of online courses. As the New York Times-bestselling authors of Big Data explain, the truly fascinating changes are actually occurring in how we measure students’ progress and how we can use that data to improve education for everyone, in real time, both on- and offline. Learning with Big Data offers an eye-opening, insight-packed tour through these new trends, for educators, administrators, and readers interested in the latest developments in business and technology.

    

    But, for the uninitiated, most of this information can be indecipherable. The Basics of Bitcoins and Blockchains aims to provide an accessible guide to this new currency and the revolutionary technology that powers it.Bitcoin, Ethereum, and other cryptocurrencies: Gain an understanding of a broad spectrum of Bitcoin topics. The Basics of Bitcoins and Blockchains covers topics such as the history of Bitcoin, the Bitcoin blockchain, and Bitcoin buying, selling, and mining. It also answers how payments are made and how transactions are kept secure. Other cryptocurrencies and cryptocurrency pricing are examined, answering how one puts a value on cryptocurrencies and digital tokens.Blockchain technology: Blockchain technology underlies all cryptocurrencies and cryptocurrency transactions. But what exactly is a blockchain, how does it work, and why is it important? The Basics of Bitcoins and Blockchains will answer these questions and more. Learn about notable blockchain platforms, smart contracts, and other important facets of blockchains and their function in the changing cyber-economy.Things to know before buying cryptocurrencies: The Basics of Bitcoins and Blockchains offers trustworthy and balanced insights to those interested in Bitcoin investing or investing in other cryptocurrency. Discover the risks and mitigations, learn how to identify scams, and understand cryptocurrency exchanges, digital wallets, and regulations with this book.Readers will learn about: Bitcoin and other cryptocurrencies Blockchain technology and how it works The workings of the cryptocurrency market The evolution and potential impacts of Bitcoin and blockchains on global businesses Dive into the world of cryptocurrency with confidence with this comprehensive introduction.

    Plus, it helps you identify areas of weakness so you can focus your study time. Sample problems and step-by-step examples with explanations in the math and physics sections show you how to work through each problem so you understand the steps it takes to complete the equation. Practice tests with answer keys for each topic - located in the appendices for quick access - help you assess your understanding of each topic and familiarize you with the types of questions you're likely to encounter on the actual exam. HESI Hints boxes offer valuable test-taking tips, as well as rationales, suggestions, examples, and reminders for specific topics.End-of-chapter review questions help you gauge your understanding of chapter content.A full-color layout and more illustrations in the life science chapters visually reinforce key concepts for better understanding.Expanded and updated content in each chapter ensures you're studying the most current content.Basic algebra review in the math section offers additional review and practice.Color-coded chapters help you quickly find specific topic sections.Helpful organizational features in each chapter include an introduction, key terms, chapter outline, and a bulleted chapter summary to help you focus your study.A glossary at the end of the text offers quick access to key terms and their definitions.

    Integrated central case studies woven throughout each chapter, use real-life stories to give you a tangible and engaging framework around which to learn and understand the science behind environmental issues. Printed on FSC (Forest Stewardship Council) certified paper, the newly revised Fourth Edition engages you through the addition of new EnvisionIt photo essays.

    The book uses the theme of evolution to develop a broad-scale view of animal diversity--students focus not only the organisms themselves, but also the processes that produce evolutionary diversity. The book is unique in its comprehensive survey of zoological diversity and its emphasis on evolutionary, systematic and ecological principles, all in one package.

    Now that machine learning is thriving, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn how to use a range of techniques, starting with simple Linear Regression and progressing to Deep Neural Networks. If you have some programming experience and you’re ready to code a machine learning project, this guide is for you.This hands-on book shows you how to use:Scikit-Learn, an accessible framework that implements many algorithms efficiently and serves as a great machine learning entry pointTensorFlow, a more complex library for distributed numerical computation, ideal for training and running very large neural networksPractical code examples that you can apply without learning excessive machine learning theory or algorithm details

    This Self-Paced Training Kit is designed to help maximize your performance on 70-680, the required exam for the Microsoft Certified Technology Specialist (MCTS): Windows 7, Configuration certification.This 2-in-1 kit includes the official Microsoft study guide, plus practice tests on CD to help you assess your skills. It comes packed with the tools and features exam candidates want most—including in-depth, self-paced training based on final exam content; rigorous, objective-by-objective review; exam tips from expert, exam-certified authors; and customizable testing options. It also provides real-world scenarios, case study examples, and troubleshooting labs to give you the skills and expertise you can use on the job.Work at your own pace through the lessons and lab exercises. This official study guide covers installing, upgrading, and migrating to Windows 7; configuring network connectivity, applications, and devices; implementing backup and recovery; configuring User Account Control (UAC), mobility options, and new features such as DirectAccess and BranchCache; and managing system updates.Then assess yourself using the 200 practice questions on CD, featuring multiple customizable testing options to meet your specific needs. Choose timed or untimed testing mode, generate random tests, or focus on discrete objectives. You get detailed explanations for right and wrong answers—including pointers back to the book for further study. You also get an exam discount voucher—making this kit an exceptional value and a great career investment.

    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.

    To meet it, you need a fully integrated text and supplements package that sets the stage for a perfectly choreographed learning experience.

    Difficult issues need to be figured out, such as scalability, consistency, reliability, efficiency, and maintainability. In addition, we have an overwhelming variety of tools, including relational databases, NoSQL datastores, stream or batch processors, and message brokers. What are the right choices for your application? How do you make sense of all these buzzwords?In this practical and comprehensive guide, author Martin Kleppmann helps you navigate this diverse landscape by examining the pros and cons of various technologies for processing and storing data. Software keeps changing, but the fundamental principles remain the same. With this book, software engineers and architects will learn how to apply those ideas in practice, and how to make full use of data in modern applications. Peer under the hood of the systems you already use, and learn how to use and operate them more effectively Make informed decisions by identifying the strengths and weaknesses of different tools Navigate the trade-offs around consistency, scalability, fault tolerance, and complexity Understand the distributed systems research upon which modern databases are built Peek behind the scenes of major online services, and learn from their architectures

    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.

    The long-anticipated revision of this best-selling text offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. *NEW-Nontechnical learning material-Accompanies each part of the book. *NEW-The Internet as a sample application for intelligent systems-Added in several places including logical agents, planning, and natural language. *NEW-Increased coverage of material - Includes expanded coverage of: default reasoning and truth maintenance systems, including multi-agent/distributed AI and game theory; probabilistic approaches to learning including EM; more detailed descriptions of probabilistic inference algorithms. *NEW-Updated and expanded exercises-75% of the exercises are revised, with 100 new exercises. *NEW-On-line Java software. *Makes it easy for students to do projects on the web using intelligent agents. *A unified, agent-based approach to AI-Organizes the material around the task of building intelligent agents. *Comprehensive, up-to-date coverage-Includes a unified view of the field organized around the rational decision making pa

    I. is explored and explained in this best selling text. Assuming no prior knowledge, it covers topics like neural networks and robotics. This text explores the range of problems which have been and remain to be solved using A. I. tools and techniques. The second half of this text is an excellent reference.