Coauthored by the designer of the Scala language, this authoritative book will teach you, one step at a time, the Scala language and the ideas behind it. The book is carefully crafted to help you learn. The first few chapters will give you enough of the basics that you can already start using Scala for simple tasks. The entire book is organized so that each new concept builds on concepts that came before - a series of steps that promises to help you master the Scala language and the important ideas about programming that Scala embodies. A comprehensive tutorial and reference for Scala, this book covers the entire language and important libraries.

    Thomas Frank, founder of the College Info Geek blog, YouTube channel, and podcast, breaks these ways down into ten steps in this short book.You'll learn how to learn more effectively in your classes, take better notes, remember more from textbook readings, cut down on procrastination, build an optimal study environment, and more.Along the way, you'll find techniques for increasing your study and work efficiency, giving you more free time in college as well.

    This book is an introduction to the concepts of computer programming within the context of the visual arts. It offers a comprehensive reference and text for Processing (www.processing.org), an open-source programming language that can be used by students, artists, designers, architects, researchers, and anyone who wants to program images, animation, and interactivity. The ideas in Processing have been tested in classrooms, workshops, and arts institutions, including UCLA, Carnegie Mellon, New York University, and Harvard University. Tutorial units make up the bulk of the book and introduce the syntax and concepts of software (including variables, functions, and object-oriented programming), cover such topics as photography and drawing in relation to software, and feature many short, prototypical example programs with related images and explanations. More advanced professional projects from such domains as animation, performance, and typography are discussed in interviews with their creators. "Extensions" present concise introductions to further areas of investigation, including computer vision, sound, and electronics. Appendixes, references to additional material, and a glossary contain additional technical details. Processing can be used by reading each unit in order, or by following each category from the beginning of the book to the end. The Processing software and all of the code presented can be downloaded and run for future exploration.Includes essays by Alexander R. Galloway, Golan Levin, R. Luke DuBois, Simon Greenwold, Francis Li, and Hernando Barragan and interviews with Jared Tarbell, Martin Wattenberg, James Paterson, Erik van Blockland, Ed Burton, Josh On, Jurg Lehni, Auriea Harvey and Michael Samyn, Mathew Cullen and Grady Hall, Bob Sabiston, Jennifer Steinkamp, Ruth Jarman and Joseph Gerhardt, Sue Costabile, Chris Csikszentmihalyi, Golan Levin and Zachary Lieberman, and Mark Hansen.Casey Reas is Associate Professor in the Design Media Arts Department at the University of California, Los Angeles. Ben Fry is Nierenburg Chair of Design in the School of Design at Carnegie Mellon University, 2006-2007."

    This edition provides increased coverage of taste and smell, circadian rhythms, brain development, and developmental disorders and includes new information on molecular mechanisms and functional brain imaging. Path of Discovery boxes, written by leading researchers, highlight major current discoveries. In addition, readers will be able to assess their knowledge of neuroanatomy with the Illustrated Guide to Human Neuroanatomy, which includes a perforated self-testing workbook.This edition's robust ancillary package includes a bound-in student CD-ROM, an Instructor's Resource CD-ROM, and resources online.

    Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools.Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python.With this handbook, you’ll learn how to use: * IPython and Jupyter: provide computational environments for data scientists using Python * NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python * Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python * Matplotlib: includes capabilities for a flexible range of data visualizations in Python * Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms

    It provides a realistic, informative, and positive introduction to the practice of chemical engineering.

    It presents the fundamentals in a clear manner, using a visual approach and real life applications.

    The Seventh Edition has been carefully updated and revised, adding a wealth of new applications and examples that analyze the important lessons offered by eBay, Google, Verizon, IBM, Microsoft, Bank of America, drug companies, the Yellow Pages, and more.

    Its organization and thoroughness are unsurpassed, putting it above similar review books. Students who thoroughly read this book should have no trouble successfully completing their psychiatry clerkship and passing the shelf exam. As course director for the core psychiatry clerkship at my institution, I will recommend this book to students."--Doody's Review Service"First Aid for the Psychiatry Clerkship" gives you the core information needed to impress on the wards and pass the psychiatry clerkship exam. Written by students who know what it takes to succeed, and based on the national guidelines for the psychiatry clerkship, the book is filled with mnemonics, ward and exam tips, tables, clinical images, algorithms, and newly added mini-cases.Features Completely revised based on the psychiatry clerkship's core competencies Written by medical students who passed and reviewed by faculty for accuracy NEW integrated mini-cases illustrate classic patient presentations and/or commonly tested scenarios NEW illustrations and management algorithms Updated throughout with enhanced sections on medications, depression/anxiety, and child psychiatry Helps students hone in on the most important concepts for the clerkship and the examThe content you need to ace the clerkship: Section I: How to Succeed in the Psychiatry Clerkship Section II: High-Yield Facts; Examination and Diagnosis; Psychotic Disorders; Mood Disorders; Anxiety and Adjustment Disorders; Personality Disorders; Substance-Related Disorders; Cognitive Disorders; Geriatric Disorders; Psychiatric Disorders in Children; Dissociative Disorders; Somataform and Factitious Disorders; Impulse Control Disorders; Eating Disordes; Disorders; Sleep Disorders; Sexual Disorders; Psychtherapies; Psychopharmacology; Legal Issues; Section III: Awards and Opportunities.

    Its techniques are widely applied in engineering, science, finance, and commerce. This book is designed for a short course on machine learning. It is a short course, not a hurried course. From over a decade of teaching this material, we have distilled what we believe to be the core topics that every student of the subject should know. We chose the title `learning from data' that faithfully describes what the subject is about, and made it a point to cover the topics in a story-like fashion. Our hope is that the reader can learn all the fundamentals of the subject by reading the book cover to cover. ---- Learning from data has distinct theoretical and practical tracks. In this book, we balance the theoretical and the practical, the mathematical and the heuristic. Our criterion for inclusion is relevance. Theory that establishes the conceptual framework for learning is included, and so are heuristics that impact the performance of real learning systems. ---- Learning from data is a very dynamic field. Some of the hot techniques and theories at times become just fads, and others gain traction and become part of the field. What we have emphasized in this book are the necessary fundamentals that give any student of learning from data a solid foundation, and enable him or her to venture out and explore further techniques and theories, or perhaps to contribute their own. ---- The authors are professors at California Institute of Technology (Caltech), Rensselaer Polytechnic Institute (RPI), and National Taiwan University (NTU), where this book is the main text for their popular courses on machine learning. The authors also consult extensively with financial and commercial companies on machine learning applications, and have led winning teams in machine learning competitions.

    The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality.Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. For each class of models, the text describes the three fundamental cornerstones: representation, inference, and learning, presenting both basic concepts and advanced techniques. Finally, the book considers the use of the proposed framework for causal reasoning and decision making under uncertainty. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. Instructors (and readers) can group chapters in various combinations, from core topics to more technically advanced material, to suit their particular needs.

    In clear, lively, warm prose, this edition continues the title's integration of gender, culture, and ethnicity. By the end, readers will learn how to interpret research and to address and resolve controversies. MyPsychLab is an integral part of the Wade/Tavris/Garry program. Engaging activities and assessments provide a teaching and learning system that helps students think like a psychologist. With MyPsychLab, students can watch videos on psychological research and applications, participate in virtual classic experiments, and develop critical thinking skills through writing. "Invitation to Psychology, "5/e is available in a new DSM-5 Updated edition. To learn more, click here. This title is available in a variety of formats - digital and print. Pearson offers its titles on the devices students love through Pearson's MyLab products, CourseSmart, Amazon, and more.

    This edition contains NEW tables on Properties of Ionic Liquids, Solubilities of Hydrocarbons in Sea Water, Solubility of Organic Compounds in Superheated Water, and Nutritive Value of Foods. It also updates many tables including Critical Constants, Heats of Vaporization, Aqueous Solubility of Organic Compounds, Vapor Pressure of Mercury, Scientific Abbreviations and Symbols, and Bond Dissociation Energies. The 88th Edition also presents a new Foreword written by Dr. Harold Kroto, a 1996 Nobel Laureate in Chemistry.

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

    This book covers relevant geometric principles and how to represent objects algebraically so they can be computed and applied. Recent major developments in the theory and practice of scene reconstruction are described in detail in a unified framework. Richard Hartley and Andrew Zisserman provide comprehensive background material and explain how to apply the methods and implement the algorithms. First Edition HB (2000): 0-521-62304-9