Deep learning /
Goodfellow, Ian.
Deep learning / Ian Goodfellow, Yoshua Bengio and Aaron Courville. - First edition. - Massachusetts : The MIT Press, 2017. - xiv, 785 pages : illustrations ; 24 cm. - Adaptive computation and machine learning .
Includes bibliographical references (pages 711-766) and index.
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.
9780262035613 (hardcover : alk. paper) 0262035618 (hardcover : alk. paper)
2016022992
Machine learning.
Applied math and machine learning basics--Linear algebra.--Probability and information theory--Machine learning basics
Deep networks : modern practices. Deep feedforward networks
Q325.5 / .G66 2016
006.3/1 G651d 2017
Deep learning / Ian Goodfellow, Yoshua Bengio and Aaron Courville. - First edition. - Massachusetts : The MIT Press, 2017. - xiv, 785 pages : illustrations ; 24 cm. - Adaptive computation and machine learning .
Includes bibliographical references (pages 711-766) and index.
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.
9780262035613 (hardcover : alk. paper) 0262035618 (hardcover : alk. paper)
2016022992
Machine learning.
Applied math and machine learning basics--Linear algebra.--Probability and information theory--Machine learning basics
Deep networks : modern practices. Deep feedforward networks
Q325.5 / .G66 2016
006.3/1 G651d 2017