TheoryofDeepLearning.2022

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IntroductionThis monograph discusses the emerging theory of deep learning. It isbased upon a graduate seminar taught at Princeton University in Fall2019 in conjunction with a Special Year on Optimization, Statistics,and Machine Learning at the Institute for Advanced Study.
Page 1: C O N T R I B U T O R S : R A M A N
Page 4 and 5: 44 Basics of generalization theory
Page 6 and 7: 612 Representation Learning 11113 E
Page 8 and 9: 810.2 Autoencoder defined using a d
Page 13 and 14: 1Basic Setup and some math notionsT
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Page 26 and 27: 26 theory of deep learning3.1.2 Nai
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Page 39: 5Advanced Optimization notionsThis
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Page 57 and 58: 7Tractable Landscapes for Nonconvex
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tractable landscapes for nonconvex
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8Ultra-wide Neural Networks and Neu
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ultra-wide neural networks and neur
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94 theory of deep learningso that M
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13Examples of Theorems, Proofs, Alg
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examples of theorems, proofs, algor
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TheoryofDeepLearning.2022

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