Deep learning theory lecture notes

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Deep learning theory lecture notes Matus Telgarsky 2021-10-27 (alpha). Contents Preface 3. Basic setup: feedforward networks and test error decomposition . . . . . . . . . . . . . . . 4. Highlights . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6. Missing topics and references . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7. 1 Approximation: preface 7. Omitted topics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8. 2 Classical approximations and universal approximation 8. Elementary folklore constructions.

Deeplearningtheorylecturenotes Matus Telgarsky mjt@illinois.edu 2021-10-27 v0.0-e7150f2d (alpha) Contents Preface 3 Basicsetup ...

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