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.

    

    This clear, concise look at the basic principles and concepts of genetics uses a human genetics perspective to discuss the methods and experiments upon which genetic principles are based, such as DNA replication.

    This guidebook is aimed clearly at the needs of the student, with a thorough understanding of, and provision for, the potential conceptual difficulties he or she is likely to encounter.

    Includes chapters on orbital mechanics, spacecraft construction, satellite-path radio wave propagation, modulation techniques, multiple access, and a detailed analysis of the communications link.

    Full of advice on how surgeons should use their heads as well as their hands - how to think, plan, and improvise - when, for example, operating on a massively bleeding trauma patient. Starts with general principles, continues with specific injuries to abdomen, chest, neck, and peripheral vessels. Generously illustrated throughout, with drawings produced specifically for this book. For residents, general surgeons with an interest in trauma, and for surgeons operating on badly wounded patients in isolated military, rural, or humanitarian settings. Asher Hirshberg and Kenneth L Mattox are trauma surgeons at the Ben Taub General Hospital, Houston, and professors at the Michael E DeBakey Department of Surgery, Baylor College of Medicine, Houston, USA. Kenneth L Maddox is famous as the lead editor of McGraw Hill's classic text, Trauma, now in its fifth edition. This is going to be a GREAT book!

    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

    The realistic content of its examples and exercises, the clarity and brevity of its presentation, and the soundness of its pedagogical approach have received the highest remarks from both students and instructors. Now this bestseller is available in a new 6th edition.

    D.S. Malik is ideal for a one-semester course focused on data structures. Clearly written with the student in mind, this text focuses on Data Structures and includes advanced topics in C++ such as Linked Lists and the Standard Template Library (STL). This student-friendly text features abundant Programming Examples and extensive use of visual diagrams to reinforce difficult topics. Students will find Dr. Malik's use of complete programming code and clear display of syntax, explanation, and example easy to read and conducive to learning.

    But the good news is that more men are being cured of this disease than ever before. Now in a revised fourth edition, this lifesaving guide by Dr. Patrick Walsh and award-winning science writer Janet Farrar Worthington offers a message of hope to every man facing this illness. Prostate cancer is a different disease in every man--which means that the right treatment varies for each person. Public awareness for this disease has transformed treatment and opened up new avenues of research; rapid advances in knowledge are being translated in new recommendations for management. In this book, Dr. Walsh will address questions such as: What causes prostate cancer? Your risk factors, including heredity, diet, and environment. Can I prevent prostate cancer? How some simple changes in your diet and lifestyle can help prevent or delay the disease. Does prostate cancer need to be treated at all? This hot-button issue is vital for men to understand. How do I know if I have prostate cancer? An explanation of the recently refined and expanded recommendations. How can my prostate cancer be treated? The pros and cons of new technologies and new information on focal therapy.

    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 bestselling textbook inorganic chemistry text on the market covers both theoretical and descriptive aspects of the subject, and emphasizes experimental methods, industrial applications, and modern topics.

    We are living in an age where data comes in abundance, and thanks to the self-learning algorithms from the field of machine learning, we can turn this data into knowledge. Automated speech recognition on our smart phones, web search engines, e-mail spam filters, the recommendation systems of our favorite movie streaming services – machine learning makes it all possible.Thanks to the many powerful open-source libraries that have been developed in recent years, machine learning is now right at our fingertips. Python provides the perfect environment to build machine learning systems productively.This book will teach you the fundamentals of machine learning and how to utilize these in real-world applications using Python. Step-by-step, you will expand your skill set with the best practices for transforming raw data into useful information, developing learning algorithms efficiently, and evaluating results.You will discover the different problem categories that machine learning can solve and explore how to classify objects, predict continuous outcomes with regression analysis, and find hidden structures in data via clustering. You will build your own machine learning system for sentiment analysis and finally, learn how to embed your model into a web app to share with the world

    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.

    The novel approach taken here banishes determinants to the end of the book and focuses on the central goal of linear algebra: understanding the structure of linear operators on vector spaces. The author has taken unusual care to motivate concepts and to simplify proofs. For example, the book presents - without having defined determinants - a clean proof that every linear operator on a finite-dimensional complex vector space (or an odd-dimensional real vector space) has an eigenvalue. A variety of interesting exercises in each chapter helps students understand and manipulate the objects of linear algebra. This second edition includes a new section on orthogonal projections and minimization problems. The sections on self-adjoint operators, normal operators, and the spectral theorem have been rewritten. New examples and new exercises have been added, several proofs have been simplified, and hundreds of minor improvements have been made throughout the text.