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.

    It will provide scholars with a comprehensive and inclusive overview of the most important student development theories and related research, including new approaches with which they may not be familiar, particularly related to social identity development. Most importantly, it will assist student affairs professionals in designing individual, group, and institutional approaches to work more effectively with students at various developmental levels and to facilitate student growth. This second edition includes the "foundational theories" of student development found in the first edition, but also offers newer integrative social identity theories that look at student development in a more holistic way. These theories are critical for understanding the diverse student populations of the twenty-first century.TABLE OF CONTENTSPrefacePart One: Understanding and Using Student Development TheoryPart Two: Foundational TheoriesPart Three: Integrative TheoriesPart Four: Social Identity DevelopmentReferences

    Reviewed by 27 of the field's leading experts, Corey's Seventh Edition covers the major concepts of counseling theories, shows students how to apply those theories in practice, and helps them learn to integrate the theories into an individualized counseling style. Incorporating the thinking, feeling, and behaving dimensions of human experience, Corey offers an easy-to-understand text that helps students compare and contrast the therapeutic models. This book is the center of a suite of products that include a revised student manual, a revised casebook, a companion text, and an all-new CD-ROM.

    For over thirty years, instructors and students have relied on it to examine drugs and behavior from the behavioral, pharmacological, historical, social, legal, and clinical perspectives.

    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

    Each essay is written by an experienced teacher/scholar and describes one of the major pedagogies employed today: process, expressive, rhetorical, collaborative, feminist, critical, cultural studies, community service, and basic writing. Writing centers, writing across the curriculum, and technology and the teaching of writing are also discussed. The essays are composed of personal statements on pedagogical applications and bibliographical guides that aid students and new teachers in further study and research. Contributors include Christopher Burnham, William A. Covino, Ann George, Diana George, Eric H. Hobson, Rebecca Moore Howard, Susan C. Jarratt, Laura Julier, Susan McLeod, Charles Moran, Deborah Mutnick, Lad Tobin, and John Trimbur. An invaluable tool for graduate students and new teachers, A Guide to Composition Pedagogies provides an exceptional introduction to composition studies and the extensive range of pedagogical approaches used today.

    A six-step model offers invaluable assistance for selecting a topic, searching the literature, developing arguments, surveying the literature, critiquing the literature, and writing the literature review.

    He solidified his standing as the nation's foremost political forecaster with his near perfect prediction of the 2012 election. Silver is the founder and editor in chief of FiveThirtyEight.com. Drawing on his own groundbreaking work, Silver examines the world of prediction, investigating how we can distinguish a true signal from a universe of noisy data. Most predictions fail, often at great cost to society, because most of us have a poor understanding of probability and uncertainty. Both experts and laypeople mistake more confident predictions for more accurate ones. But overconfidence is often the reason for failure. If our appreciation of uncertainty improves, our predictions can get better too. This is the "prediction paradox": The more humility we have about our ability to make predictions, the more successful we can be in planning for the future.In keeping with his own aim to seek truth from data, Silver visits the most successful forecasters in a range of areas, from hurricanes to baseball, from the poker table to the stock market, from Capitol Hill to the NBA. He explains and evaluates how these forecasters think and what bonds they share. What lies behind their success? Are they good-or just lucky? What patterns have they unraveled? And are their forecasts really right? He explores unanticipated commonalities and exposes unexpected juxtapositions. And sometimes, it is not so much how good a prediction is in an absolute sense that matters but how good it is relative to the competition. In other cases, prediction is still a very rudimentary-and dangerous-science.Silver observes that the most accurate forecasters tend to have a superior command of probability, and they tend to be both humble and hardworking. They distinguish the predictable from the unpredictable, and they notice a thousand little details that lead them closer to the truth. Because of their appreciation of probability, they can distinguish the signal from the noise.

    It is designed as a reference for students in the physical sciences and engineering.

    The title says it is the hard way to learn to writecode but it’s actually not. It’s the “hard” way only in that it’s the way people used to teach things. In this book youwill do something incredibly simple that all programmers actually do to learn a language: 1. Go through each exercise. 2. Type in each sample exactly. 3. Make it run.That’s it. This will be very difficult at first, but stick with it. If you go through this book, and do each exercise for1-2 hours a night, then you’ll have a good foundation for moving on to another book. You might not really learn“programming” from this book, but you will learn the foundation skills you need to start learning the language.This book’s job is to teach you the three most basic essential skills that a beginning programmer needs to know:Reading And Writing, Attention To Detail, Spotting Differences.

    The American Dream champions individualism.  But at what price?  In this timely revision of The Wilding of America, Charles Derber chronicles the latest incidents of "wilding" - extreme acts of self-interested violence and greed - that signal an eroding of the moral landscape of American society.  Despite this ever-increasing emphasis on individualism in America, Derber offers a communitarian alternative that is as inspiring as it is instructive.

    Donna is so sensitive in exploring those issues, a first in a text for that class and a welcome addition.--Nick Eastmond, Utah State UniversityFocused on discussing what is considered to be good research, this text explains quantitative, qualitative, and mixed methods in detail, incorporating the viewpoints of various research paradigms into the descriptions of these methods. Approximately 60% of the content in this Third Edition is new, with lots of fresh examples.Key FeaturesPostpositivist, constructivist, transformative, and pragmatic paradigms discussedConducting research in culturally complex communities emphasized throughoutA step-by-step overview of the entire research process providedNew to this Edition New coverage on how to write a literature review and plan a dissertationNew pedagogy including Extending Your Thinking throughoutThis is a core or supplemental text for research courses in departments of education, psychology, sociology, social work and other human-services disciplines.

    Therefore, this book provides the fundamental concepts and techniques of real analysis for readers in all of these areas. It helps one develop the ability to think deductively, analyze mathematical situations and extend ideas to a new context. Like the first two editions, this edition maintains the same spirit and user-friendly approach with some streamlined arguments, a few new examples, rearranged topics, and a new chapter on the Generalized Riemann Integral.

    These leading scholars, each representing diverse academic traditions, have developed a unified approach to valid descriptive and causal inference in qualitative research, where numerical measurement is either impossible or undesirable. Their book demonstrates that the same logic of inference underlies both good quantitative and good qualitative research designs, and their approach applies equally to each.Providing precepts intended to stimulate and discipline thought, the authors explore issues related to framing research questions, measuring the accuracy of data and uncertainty of empirical inferences, discovering causal effects, and generally improving qualitative research. Among the specific topics they address are interpretation and inference, comparative case studies, constructing causal theories, dependent and explanatory variables, the limits of random selection, selection bias, and errors in measurement. Mathematical notation is occasionally used to clarify concepts, but no prior knowledge of mathematics or statistics is assumed. The unified logic of inference that this book explicates will be enormously useful to qualitative researchers of all traditions and substantive fields.

    In 2007, Janet Belsky's "Experiencing the Lifespan" was published to widespread instructor and student acclaim, ultimately winning the 2008 Textbook Excellence Award from the Text and Academic Authors Association. Now that breakthrough text returns in a rigorously updated edition that explores the lifespan by combining the latest research with a practicing psychologist's understanding of people, and a teacher's understanding of students and classroom dynamics. And again, all of this in the right number of pages to fit comfortably in a single term course.