An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. 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.

With millions of copies sold, the Publication Manual of the American Psychological Association is the style manual of choice for writers, editors, students, educators, and professionals in psychology, sociology, business, economics, nursing, social work, and justice administration, and other disciplines in which effective communication with words and data is fundamental. In addition to providing clear guidance on grammar, the mechanics of writing, and APA style, the Publication Manual offers an authoritative and easy-to-use reference and citation system and comprehensive coverage of the treatment of numbers, metrication, statistical and mathematical data, tables, and figures for use in writing, reports, or presentations. The new edition has been revised and updated to include: The latest guidelines and examples for referencing electronic and online sources; New and revised guidelines for submitting papers electronically; Improved guidelines for avoiding plagiarism; Simplified formatting guidelines for writers using up-to-date word-processing software; All new guidelines for presenting case studies; Improved guidelines for the construction of tables; Updates on copyright and permissions issues for writers. New reference examples for audiovisual media and patents; An expanded and improved index for quick and easy access; Writers, scholars, and professionals will also find: New guidelines on how to choose text, tables, or figures to present data; Guidelines for writing cover letters for submitting articles for publication, plus a sample letter; Expanded guidelines on the retention of raw data; New advice on establishing written agreements for the use of shared data; New information on the responsibilities of co-authors.--From the publisher.

The Publication Manual of the American Psychological Association is the style manual of choice for writers, editors, students, and educators in the social and behavioral sciences, nursing, education, business, and related disciplines.

Introduction to Art: Design, Context, and Meaning offers a deep insight and comprehension of the world of Art. Contents: What is Art? The Structure of Art Significance of Materials Used in Art Describing Art - Formal Analysis, Types, and Styles of Art Meaning in Art - Socio-Cultural Contexts, Symbolism, and Iconography Connecting Art to Our Lives Form in Architecture Art and Identity Art and Power Art and Ritual Life - Symbolism of Space and Ritual Objects, Mortality, and Immortality Art and Ethics

New York Times bestselling authors Karen White, Beatriz Williams, and Lauren Willig present a masterful collaboration—a rich, multigenerational novel of love and loss that spans half a century.... 1945: When critically wounded Captain Cooper Ravenel is brought to a private hospital on Manhattan’s Upper East Side, young Dr. Kate Schuyler is drawn into a complex mystery that connects three generations of women in her family to a single extraordinary room in a Gilded Age mansion. Who is the woman in Captain Ravenel’s miniature portrait who looks so much like Kate? And why is she wearing the ruby pendant handed down to Kate by her mother? In their pursuit of answers, they find themselves drawn into the turbulent stories of Olive Van Alan, driven in the Gilded Age from riches to rags, who hired out as a servant in the very house her father designed, and Lucy Young, who in the Jazz Age came from Brooklyn to Manhattan seeking the father she had never known. But are Kate and Cooper ready for the secrets that will be revealed in the Forgotten Room? READERS GUIDE INCLUDED

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.

This unique work compiles the latest knowledge around veterinary nutraceuticals, commonly referred to as dietary supplements, from ingredients to final products in a single source. More than sixty chapters organized in seven sections collate all related aspects of nutraceutical research in animal health and disease, among them many novel topics: common nutraceutical ingredients (Section-I), prebiotics, probiotics, synbiotics, enzymes and antibacterial alternatives (Section-II), applications of nutraceuticals in prevention and treatment of various diseases such as arthritis, periodontitis, diabetes, cognitive dysfunctions, mastitis, wounds, immune disorders, and cancer (Section-III), utilization of nutraceuticals in specific animal species (Section-IV), safety and toxicity evaluation of nutraceuticals and functional foods (Section-V), recent trends in nutraceutical research and product development (Section-VI), as well as regulatory aspects for nutraceuticals (Section-VII). The future of nutraceuticals and functional foods in veterinary medicine seems bright, as novel nutraceuticals will emerge and new uses of old agents will be discovered. International contributors to this book cover a variety of specialties in veterinary medicine, pharmacology, pharmacognosy, toxicology, chemistry, medicinal chemistry, biochemistry, physiology, nutrition, drug development, regulatory frameworks, and the nutraceutical industry. This is a highly informative and carefully presented book, providing scientific insight for academia, veterinarians, governmental and regulatory agencies with an interest in animal nutrition, complementary veterinary medicine, nutraceutical product development and research.