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#94 – Ilya Sutskever: Deep Learning

Lex Fridman Podcast

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Understanding Deep Double Descent in Neural Networks

This chapter explores the intriguing phenomenon of deep double descent in deep learning, revealing how larger models can initially underperform before achieving improved performance. It examines the interplay between model size, overfitting, and data characteristics, shedding light on effective training strategies like early stopping to enhance model learning.

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