CS 559 Deep Learning (Spring 2017)
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Week | Lectures and presentations | Resources |
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W1a Feb 8 |
Lecture:
| Part I of Deep Learning by Goodfellow et al. |
W1b Feb 10 |
Lecture:
| Python is strongly recommended for the projects and homeworks: an excellent tutorial by Justin Johnson. |
W2a Feb 15 |
Lecture:
| Lecture notes by Andrej Karpathy: loss functions |
W2b Feb 17 |
Lecture:
| Lecture notes by Andrej Karpathy: optimization |
W3a Feb 22 |
Lecture:
| |
W3b Feb 24 |
Lecture:
| Section 2.6-2.8 of Deep Learning by Goodfellow et al. [optional] |
W4a Mar 1 |
Lecture:
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W4b Mar 3 |
Lecture:
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W5a Mar 8 |
Lecture:
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W5b Mar 10 |
Paper presentations:
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W6a Mar 15 |
Lecture:
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W6b Mar 17 |
Paper presentations:
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W7a Mar 22 (+1h) |
Lecture:
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W7b Mar 24 |
No class.
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W8a Mar 29 |
Lecture:
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W8b Mar 31 |
Paper presentations:
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W9a Apr 5 (+1h) |
Lecture:
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W9b Apr 7 |
Midterm:
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W10a Apr 12 |
Lecture:
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W10b Apr 14 |
Paper presentations:
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W11a Apr 19 |
Lecture:
| Tutorial on Variational Autoencoders |
W11b Apr 21 |
Project progress presentations
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W12a Apr 26 |
Lecture:
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W12b Apr 28 |
Paper presentations:
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W13a May 3 |
Paper presentations:
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W13b May 5 |
Paper presentations:
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W14a May 10 |
Paper presentations:
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W14b May 12 |
Project final presentations
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Projects can be done individually or as a group. The goal of the projects will be to explore novel applications of contemporary deep learning techniques or develop novel deep learning techniques. Projects related to research topics of the students are encouraged.