Deep Learning
Deep Learning

Author: Ian Goodfellow

Publisher: MIT Press

Published: 2016-11-10

Total Pages: 801

ISBN-13: 0262337371

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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.

An Introduction to Statistical Learning
An Introduction to Statistical Learning

Author: Gareth James

Publisher: Springer Nature

Published: 2023-08-01

Total Pages: 617

ISBN-13: 3031387473

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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.

Publication Manual of the American Psychological Association
Publication Manual of the American Psychological Association

Author: American Psychological Association

Publisher: American Psychological Association (APA)

Published: 2019-10

Total Pages: 428

ISBN-13: 9781433832161

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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.

The Forgotten Room
The Forgotten Room

Author: Karen White

Publisher: Penguin

Published: 2016-01-19

Total Pages: 386

ISBN-13: 0698191013

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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

Introduction to Art: Design, Context, and Meaning
Introduction to Art: Design, Context, and Meaning

Author: Pamela Sachant

Publisher: Good Press

Published: 2023-11-27

Total Pages: 614

ISBN-13:

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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

Bayesian Data Analysis, Third Edition
Bayesian Data Analysis, Third Edition

Author: Andrew Gelman

Publisher: CRC Press

Published: 2013-11-01

Total Pages: 677

ISBN-13: 1439840954

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Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.