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.
General
Imprint: |
MIT Press
|
Country of origin: |
United States |
Series: |
Deep Learning |
Release date: |
November 2016 |
First published: |
2016 |
Authors: |
Ian Goodfellow
(Senior Research Scientist)
• Yoshua Bengio
(Full Professor)
• Aaron Courville
(Assistant Professor)
|
Dimensions: |
236 x 185 x 34mm (L x W x T) |
Format: |
Hardcover
|
Pages: |
800 |
ISBN-13: |
978-0-262-03561-3 |
Categories: |
Books >
Computing & IT >
General
|
LSN: |
0-262-03561-8 |
Barcode: |
9780262035613 |
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