| Math Theory Apps Deep Learning - 22431 - MA 59800 - 529 |
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Associated Term: Fall 2020
Levels: Undergraduate, Graduate, Professional West Lafayette Campus Lecture Schedule Type Learning Outcomes: Part I: deep learning basics: feed-forward networks; convolutional networks; recurrent neural networks; deep reinforcement learning; Part II: deep learning applications: data-driven recovery of equations; solving partial differential equations; generative models; Part III: deep learning theory: approximation theory, optimization theory, and generalization theory of deep learning. Required Materials: Textbook - Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Go to https://www.deeplearningbook.org for a free online textbook. Technical Requirements: Students must review basic numerical linear algebra, differential equations, probability, and optimization by themselves. View Catalog Entry
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