Deep learning
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 -- Convolution...
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| 主要作者: | |
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| 其他作者: | |
| 格式: | 圖書 |
| 語言: | Undetermined |
| 出版: |
Cambridge, Massachusetts
The MIT Press
[2016]
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| 主題: | |
| 在線閱讀: | http://lrc.tdmu.edu.vn/opac/search/detail.asp?aID=2&ID=41713 |
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| Thư viện lưu trữ: | Trung tâm Học liệu Trường Đại học Thủ Dầu Một |
|---|
| LEADER | 01322nam a2200217Ia 4500 | ||
|---|---|---|---|
| 001 | TDMU_41713 | ||
| 008 | 210410s9999 xx 000 0 und d | ||
| 082 | |a 006.3 | ||
| 090 | |b G432 | ||
| 100 | |a Goodfellow, Ian | ||
| 245 | 0 | |a Deep learning | |
| 245 | 0 | |c Ian Goodfellow, Yoshua Bengio, and Aaron Courville | |
| 260 | |a Cambridge, Massachusetts | ||
| 260 | |b The MIT Press | ||
| 260 | |c [2016] | ||
| 300 | |a xxii, 775 pages | ||
| 520 | |a 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. | ||
| 650 | |a Machine learning; Máy học | ||
| 700 | |a Bengio, Yoshua | ||
| 856 | |u http://lrc.tdmu.edu.vn/opac/search/detail.asp?aID=2&ID=41713 | ||
| 980 | |a Trung tâm Học liệu Trường Đại học Thủ Dầu Một | ||