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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| Format: | Buch |
| Sprache: | Undetermined |
| Veröffentlicht: |
Cambridge, Massachusetts
The MIT Press
[2016]
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| Online Zugang: | 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 |
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| Zusammenfassung: | 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. |
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| Beschreibung: | xxii, 775 pages |