Subject |
Machine learning,
|
Descript |
xxii, 775 pages : illustrations (some colour) ; 24 cm. |
Content |
text txt |
Media |
unmediated n |
Carrier |
volume nc |
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. |
Alt author |
Bengio, Yoshua, author.
|
|
Courville, Aaron, author.
|
ISBN |
9780262035613 |
|
0262035618 |
|