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Senior Project for Statistics & Data Science at Yale University

TeX 74.50% Python 10.50% Jupyter Notebook 14.96% Makefile 0.04%
deep-learning gradient-descent gradient-flow linear-regression neural-network neural-tangent-kernel optimization python tensorflow

nn-kernel-and-rich-regimes's Introduction

Implicit Regularization in Deep Learning: The Kernel and Rich Regimes

Senior Project for Statistics & Data Science at Yale University completed by Henry Smith

I am grateful to have been advised by Dr. Harrison Zhou

Code Requirements: Python 3.7-3.9, TensorFlow 2.8

View the final project report and poster.

For proofs of the main results from "On Lazy Training in Differentiable Programming" by Chizat, Oyallon, and Bach, see another report I completed for S&DS 432, Advanced Optimization Techniques.

A neural network function with two weights evaluated around its initialization

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