Deep Learning

Deep Learning
Author :
Publisher : MIT Press
Total Pages : 801
Release :
ISBN-10 : 9780262337373
ISBN-13 : 0262337371
Rating : 4/5 (73 Downloads)

Book Synopsis Deep Learning by : Ian Goodfellow

Download or read book Deep Learning written by Ian Goodfellow and published by MIT Press. This book was released on 2016-11-10 with total page 801 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.


Deep Learning Related Books

Deep Learning
Language: en
Pages: 801
Authors: Ian Goodfellow
Categories: Computers
Type: BOOK - Published: 2016-11-10 - Publisher: MIT Press

DOWNLOAD EBOOK

An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and res
Deep Learning
Language: en
Pages: 801
Authors: Ian Goodfellow
Categories: Computers
Type: BOOK - Published: 2016-11-18 - Publisher: MIT Press

DOWNLOAD EBOOK

An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and res
Deep Learning
Language: en
Pages: 0
Authors: Ian Goodfellow
Categories:
Type: BOOK - Published: 2023-04-22 - Publisher:

DOWNLOAD EBOOK

Looking for a comprehensive guide to the exciting world of deep learning? Look no further than this must-have book! Written by a team of experts, this guide off
Deep Learning for Coders with fastai and PyTorch
Language: en
Pages: 624
Authors: Jeremy Howard
Categories: Computers
Type: BOOK - Published: 2020-06-29 - Publisher: O'Reilly Media

DOWNLOAD EBOOK

Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with
Introduction to Deep Learning
Language: en
Pages: 187
Authors: Eugene Charniak
Categories: Computers
Type: BOOK - Published: 2019-01-29 - Publisher: MIT Press

DOWNLOAD EBOOK

A project-based guide to the basics of deep learning. This concise, project-driven guide to deep learning takes readers through a series of program-writing task