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Energy, entropy and the physics of deep learning

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Deep learning has achieved beyond-human accuracy in a plethora of challenging tasks, ranging from image recognition to gameplay. Nonetheless, why deep learning “works” is thus far an open question. In my talk, I will argue that physical concepts such as energy and entropy allow us to explain the surprising efficacy of deep learning. I will show that stochastic gradient descent, a machine learning algorithm that is commonly used, can be mapped to the familiar physics of Brownian motion albeit with a spatially anisotropic noise that is crucial to its success. Moreover, theoretical tools used to analyse spin glasses and energy landscapes give us important insights about the structure of loss functions that are typically encountered in deep learning.

This talk is part of the Cambridge University Physics Society series.

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