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Deep learning theory lecture notes

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 .. 9. Universal approximation with a single hidden layer .. 12. 3 Infinite-width Fourier representations and the Barron norm 14. Infinite-width univariate approximations .. 15. Barron's construction for infinite-width multivariate approximation .. 15. Sampling from infinite width networks .. 19. 4 Approximation near initialization and the Neural Tangent Kernel 23. Basic setup: Taylor expansion of shallow networks.

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

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