It includes all the diagnostic criteria from DSM-IV-TR(R) in an easy-to-use, spiralbound format. It includes a pull-out chart of the DSM-IV-TR Classification.Changes were made to the following diagnostic criteria: - Personality Change Due to a General Medical Condition - Paraphilias- Tic Disorders

    The exposition is simple and leads to the most complete direct means of solving problems in mechanics. The final sections on adiabatic invariants have been revised and augmented. In addition a short biography of L D Landau has been inserted.

    Mitchell covers the field of machine learning, the study of algorithms that allow computer programs to automatically improve through experience and that automatically infer general laws from specific data.

    One can trace its roots to the Calculus of Variations and the work of Euler and Lagrange. This natural and reasonable approach to mathematical programming covers numerical methods for finite-dimensional optimization problems. It begins with very simple ideas progressing through more complicated concepts, concentrating on methods for both unconstrained and constrained optimization.

    Brigadier General Ulysses S. Grant had no significant military successes to his credit. He was barely clinging to his position within the Union Army-he had been officially charged with chronic drunkenness only days earlier, and his own troops despised him. His opponent was as untested as he was: an obscure lieutenant colonel named Nathan Bedford Forrest. Forrest was a slaveholder, Grant a closet abolitionist-but the two men held one thing in common: an unrelenting desire for victory at any cost. After ten days of horrific battle, Grant emerged victorious. He had earned himself the nickname “Unconditional Surrender” for his fierce prosecution of the campaign, and immediately became a hero of the Union Army. Forrest retreated, but he soon re-emerged as a fearsome war machine and guerrilla fighter. His reputation as a brilliant and innovative general survives to this day. But Grant had already changed the course of the Civil War. By opening the Tennessee and Cumberland rivers to the Union Army, he had split Dixie in two. The confederacy would never recover. A riveting account of the making of two great military leaders, and two battles that transformed America forever, Men of Fire is destined to become a classic work of military history.

    You'll discover the power of storytelling and the way to make data a pivotal point in your story. The lessons in this illuminative text are grounded in theory, but made accessible through numerous real-world examples--ready for immediate application to your next graph or presentation.Storytelling is not an inherent skill, especially when it comes to data visualization, and the tools at our disposal don't make it any easier. This book demonstrates how to go beyond conventional tools to reach the root of your data, and how to use your data to create an engaging, informative, compelling story. Specifically, you'll learn how to:Understand the importance of context and audience Determine the appropriate type of graph for your situation Recognize and eliminate the clutter clouding your information Direct your audience's attention to the most important parts of your data Think like a designer and utilize concepts of design in data visualization Leverage the power of storytelling to help your message resonate with your audience Together, the lessons in this book will help you turn your data into high impact visual stories that stick with your audience. Rid your world of ineffective graphs, one exploding 3D pie chart at a time. There is a story in your data--Storytelling with Data will give you the skills and power to tell it!

    Following on the success of the first six editions of the series, this work introduces theoretical and pedagogical innovations in statics, dynamics, and mechanics of materials education.

    A comprehensive introduction to the subject, this book shows in detail how such problems can be solved numerically with great efficiency. The focus is on recognizing convex optimization problems and then finding the most appropriate technique for solving them. The text contains many worked examples and homework exercises and will appeal to students, researchers and practitioners in fields such as engineering, computer science, mathematics, statistics, finance, and economics.

    Capturing the tone of students exchanging ideas among themselves, this unique guide also explains how calculus is taught, how to get the best teachers, what to study, and what is likely to be on exams—all the tricks of the trade that will make learning the material of first-semester calculus a piece of cake. Funny, irreverent, and flexible, How to Ace Calculus shows why learning calculus can be not only a mind-expanding experience but also fantastic fun.

    Feynman starts with an intuitive view of fundamental quantum mechanics, gradually introducing path integrals. Later chapters explore more advanced topics, including the perturbation method, quantum electrodynamics, and statistical mechanics. 1965 edition, emended in 2005.

    Sipser's candid, crystal-clear style allows students at every level to understand and enjoy this field. His innovative "proof idea" sections explain profound concepts in plain English. The new edition incorporates many improvements students and professors have suggested over the years, and offers updated, classroom-tested problem sets at the end of each chapter.

    Emphasis is placed on the standardized and systematic process for initial assessment. It is the intent of the TNCC to enhance the nurse's ability to rapidly and accurately assess the patient's responses to the trauma event and to work within the context of a trauma team. It is anticipated that the knowledge and skills learned in the TNCC will ultimately contribute to a decrease in the morbidity and mortality associated with trauma. This Revised Printing of the new Sixth Edition of the TNCC's Provider Manual had been updated by members of the TNCC Revision Workgroup and trauma nursing experts in both the United States and internationally.

    This guide provides explanations of eigenvalues, eigenvectors, linear transformations, linear equations, vectors, and matrices.

    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

    Premiere authors Dennis Wackerly, William Mendenhall, and Richard L. Scheaffer present a solid foundation in statistical theory while conveying the relevance and importance of the theory in solving practical problems in the real world. The authors' use of practical applications and excellent exercises helps readers discover the nature of statistics and understand its essential role in scientific research.