Deep Learning

Deep Learning

The Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular.

Publication date: 18 Nov 2016

ISBN-10: 0262035618

ISBN-13: 9780262035613

Paperback: 800 pages

Views: 4,064

Type: Textbook

Publisher: The MIT Press

License: n/a

Post time: 16 Apr 2016 03:00:00

Deep Learning

Deep Learning The Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular.
Tag(s): Machine Learning
Publication date: 18 Nov 2016
ISBN-10: 0262035618
ISBN-13: 9780262035613
Paperback: 800 pages
Views: 4,064
Document Type: Textbook
Publisher: The MIT Press
License: n/a
Post time: 16 Apr 2016 03:00:00
From the Introduction:

In the early days of artificial intelligence, the field rapidly tackled and solved problems that are intellectually diffcult for human beings but relatively straight-forward for computers—problems that can be described by a list of formal, mathematical rules. The true challenge to artificial intelligence proved to be solving the tasks that are easy for people to perform but hard for people to describe formally—problems that we solve intuitively, that feel automatic, like recognizing spoken words or faces in images. 

This book is about a solution to these more intuitive problems. This solution isto allow computers to learn from experience and understand the world in terms of ahierarchy of concepts, with each concept defined in terms of its relation to simpler concepts. By gathering knowledge from experience, this approach avoids the need for human operators to formally specify all of the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones. If we draw a graph showing how these concepts are built on top of each other, the graph is deep, with many layers. Forthis reason, we call this approach to AI deep learning.




About The Author(s)


Yoshua Bengio is Full Professor of the Department of Computer Science and Operations Research, head of the Machine Learning Laboratory (MILA), CIFAR Program co-director of the CIFAR Neural Computation and Adaptive Perception program,  Canada Research Chair in Statistical Learning Algorithms, and he also holds the NSERC-Ubisoft industrial chair. His main research ambition is to understand principles of learning that yield intelligence. 

Yoshua Bengio

Yoshua Bengio is Full Professor of the Department of Computer Science and Operations Research, head of the Machine Learning Laboratory (MILA), CIFAR Program co-director of the CIFAR Neural Computation and Adaptive Perception program,  Canada Research Chair in Statistical Learning Algorithms, and he also holds the NSERC-Ubisoft industrial chair. His main research ambition is to understand principles of learning that yield intelligence. 


Aaron Courville is an Assistant Professor in the Department of Computer Science and Operations Research (DIRO) at the University of Montreal, and member of the LISA lab (LISA: Laboratoire d'Informatique des Systèmes Adaptatifs).

Aaron Courville

Aaron Courville is an Assistant Professor in the Department of Computer Science and Operations Research (DIRO) at the University of Montreal, and member of the LISA lab (LISA: Laboratoire d'Informatique des Systèmes Adaptatifs).


Ian Goodfellow is a Senior Research Scientist on the Google Brain team. He studies new methods for improving neural networks.

Ian Goodfellow

Ian Goodfellow is a Senior Research Scientist on the Google Brain team. He studies new methods for improving neural networks.


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