Deep learning / Ian Goodfellow, Yoshua Bengio and Aaron Courville.

Format:

Book

Author/Creator:

Goodfellow, Ian, author.

Bengio, Yoshua, author.

Courville, Aaron, author.

Series:

Adaptive computation and machine learning.

Adaptive computation and machine learning

Language:

English

Subjects (All):

Machine learning.

Artificial intelligence.

Physical Description:

1 online resource (xxii, 775 pages) : illustrations.

Place of Publication:

Cambridge, Massachusetts ; London, England : The MIT Press, [2016]

Summary:

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. 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.

Contents:

Introduction

Applied math and machine learning basics. Linear algebra

Probability and information theory

Numerical computation

Machine learning basics

Deep networks: modern practices. Deep feedforward networks

Regularization for deep learning

Optimization for training deep models

Convolutional networks

Sequence modeling: recurrent and recursive nets

Practical methodology

Applications

Deep learning research. Linear factor models

Autoencoders

Representation learning

Structured probabilistic models for deep learning

Monte Carlo methods

Confronting the partition function

Approximate inference

Deep generative models.

Notes:

Includes bibliographical references (pages 711-766) and index.

ISBN:

9780262337373

0262337371