Deep learning
Goodfellow, Ian,
Deep learning Ian Goodfellow, Yoshua Bengio, and Aaron Courville. - 775 p. illustrations (some color) ; 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
2016022992
Machine learning,
Q325.5 / .G66 2016
006.31
Deep learning Ian Goodfellow, Yoshua Bengio, and Aaron Courville. - 775 p. illustrations (some color) ; 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
2016022992
Machine learning,
Q325.5 / .G66 2016
006.31