| Description |
xxii, 775 pages : illustrations (some color) ; 24 cm. |
| Series |
Adaptive computation and machine learning
|
| Bibliography |
Includes bibliographical references (pages 711-766) and index. |
| Contents |
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. |
| Subject |
Machine learning,
|
| Added Author |
Bengio, Yoshua, author
|
|
Courville, Aaron, author
|
| ISBN |
9780262035613 (hardcover : alk. paper) |
|
0262035618 (hardcover : alk. paper) |
|
