As the field of environmental science continues to evolve, this highly readable guide presents a full spectrum of views and information for students to evaluate issues and make informed decisions. An extensive resource package integrates text and digital media in an easy-to-use format designed to assist instructors in classroom preparation.

    The authors have eliminated unnecessary mathematical detail and concentrate on the intuition and insights that will be useful to practitioners throughout their careers as new ideas and challenges emerge from the financial marketplace. Essentials maintains the theme of asset allocation (authors discuss asset pricing and trading then apply these theories to portfolio planning in real-world securities markets that are governed by risk/return relationships).

    Over the years their textbooks have introduced significant theoretical and pedagogical innovations in statics, dynamics, and mechanics of materials education. At the same time, their careful presentation of content, unmatched levels of accuracy, and attention to detail have made their texts the standard for excellence. The new Seventh Edition of Vector Mechanics for Engineers: Statics and Dynamics continues this tradition. The seventh edition is complemented by a media and supplement package that is targeted to address core course needs for both the student and the instructor.

    This book is built around the idea that students must study the discipline of psychology as a whole, that the sub-disciplines are intricately connected, and that human behavior is best understood by exploring its functioning state in addition to its potential dysfunctions.

    Updated to reflect current research and rewritten to further enhance the clarity of presentation, the fifth edition of this best-seller continues to take a linguistic-analysis approach as well and focus on the facts of language rather than theoretical approaches.

    Updated information is included about the associated features, culture, age & gender features, prevalence, course & familial pattern of mental disorders. The DSM-IV-TR(R) brings this essential diagnostic tool to-date, to promote effective diagnosis, treatment & quality of care. One can get all the essential diagnostic information from the DSM-IV(R) along with important updates not in the '94 edition. Benefit from new research into Schizophrenia, Asperger's Disorder & other conditions. Utilize additional information about the epidemiology & other facets of DSM conditions. Update ICD-9-CM codes implemented since 1994 including Conduct Disorder, Dementia, Somatoform Disorders.Use of the manual DSM-IV-TR classification Multiaxial assessment Disorders usually 1st diagnosed in infancy, childhood or adolescenceDelirium, dementia & amnestic & other cognitive disordersMental disorders due to a general medical condition Substance-related disorders Schizophrenia & other psychotic disordersMood disorders Anxiety disordersSomatoform disordersFactitious disordersDissociative disordersSexual & gender identity disordersEating disorders Sleep disorders Impulse-control disorders not elsewhere classifiedAdjustment disordersPersonality disordersOther conditions that may be a focus of clinical attentionAdditional codes Appendix A: Decision trees for differential diagnosis Appendix B: Criteria sets & axes provided for further study Appendix C: Glossary of technical terms Appendix D: Highlights of changes in DSM-IV text revisionAppendix E: Alphabetical listing of DSM-IV-TR diagnoses & codesAppendix F: Numerical listing of DSM-IV-TR diagnoses & codesAppendix G: ICD-9-CM codes for selected general medical conditions & medication-induced disordersAppendix H: DSM-IV classification with ICD-10 codesAppendix I: Outline for cultural formulation & glossary of culture-bound syndromesAppendix J: DSM-IV contributorsAppendix K: DSM-IV text revision advisers

    Carroll, a recognized leader in the field of evolutionary development, to this new edition of Introduction to Genetic Analysis (IGA). The authors’ ambitious new plans for this edition focus on showing how genetics is practiced today. In particular, the new edition renews its emphasis on how genetic analysis can be a powerful tool for answering biological questions of all types.

    This practical text reviews basic microbiology and how to approach the pharmacotherapy of a patient with a presumed infection. It also contains concise Drug Class Reviews with an explanation of the characteristics of various classes of antibacterial drugs and antifungal drugs. Antibiotics Simplified, Third Edition simplifies learning infectious disease pharmacotherapy and condenses the many facts that are taught about antibiotics into one quick reference guide. This guide will help students learn the characteristics of antibiotics and why an antibiotic is useful for an indication. With an understanding of the characteristics of the antibiotics, students will be able to make a logical choice to treat an infection more easily. With helpful figures and flow charts, Drug Class Reviews, a Spectra of Activity chart, and an index for reference, this is an ideal handbook for students as well as practicing pharmacists, physicians, and other clinicians!

    Each chapter is relatively self-contained and can be used as a unit of study. The algorithms are described in English and in a pseudocode designed to be readable by anyone who has done a little programming. The explanations have been kept elementary without sacrificing depth of coverage or mathematical rigor.

    The Fifth Edition features updated information throughout, including new theories and models such as the Healthy Action Process Approach (HAPA) and the Community Readiness Model (CRM), sections on grant writing and preparing a budget, real-life examples of marketing principles and processes, and a new classification system for evaluation approaches and designs. Health Education, Health Promotion, Health Educators, and Program Planning, Models for Program Planning in Health Promotion, Starting the Planning Process, Assessing Needs, Measurement, Measures, Measurement Instruments and Sampling, Mission Statement, Goals, and Objectives, Theories and Models Commonly Used for Health Promotion Interventions, Interventions, Community Organizing and Community Building, Identification and Allocation of Resources, Marketing: Making Sure Programs Respond to Wants and Needs of Consumers, Implementation: Strategies and Associated Concerns, Evaluation: An Overview, Evaluation Approaches and Designs, Data Analysis and Reporting. Intended for those interested in learning the basics of planning, implementing, and evaluating health promotion programs

    The book begins with an introduction to the language of statistics and then covers descriptive statistics and inferential statistics. Throughout, the author offers readers:- Difficulty Rating Index for each chapter′s material- Tips for doing and thinking about a statistical technique- Top tens for everything from the best ways to create a graph to the most effective techniques for data collection- Steps that break techniques down into a clear sequence of procedures- SPSS tips for executing each major statistical technique- Practice exercises at the end of each chapter, followed by worked out solutions.The book concludes with a statistical software sampler and a description of the best Internet sites for statistical information and data resources. Readers also have access to a website for downloading data that they can use to practice additional exercises from the book. Students and researchers will appreciate the book′s unhurried pace and thorough, friendly presentation.

    It includes: Two full-length exams that follow the content and style of the new AP exam All test questions answered and explained An extensive review covering all AP test topics Hundreds of additional multiple-choice and free-response practice questions with answer explanations

    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

    It discusses new results, along with applications of probability theory to a variety of problems. The book contains many exercises and is suitable for use as a textbook on graduate-level courses involving data analysis. Aimed at readers already familiar with applied mathematics at an advanced undergraduate level or higher, it is of interest to scientists concerned with inference from incomplete information.

    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.