Theories of Deep Learning

Per LessWrong, an essay examines mathematical frameworks now explaining why deep learning works so remarkably well. For years, the field raced ahead with stunning empirical success—scaling simply worked—while rigorous theory trailed behind. That gap is rapidly closing. The author surveys emerging theories: categorical deep learning uses abstract algebra to describe all neural network architectures in a unified framework. Modular duality tackles optimization, explaining how training algorithms behave and suggesting faster approaches. Together, these theories address key puzzles—architecture design, training mechanics, and how networks generalize from limited data.

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