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Rothschild Lecture: The mathematical universe behind deep neural networks

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MDL - Mathematics of deep learning

Deep neural networks have led to breakthrough results in numerous practical machine learning tasks such as image classification, image captioning, control-policy-learning to play the board game Go, and most recently the prediction of protein structures. In this lecture, we will attempt a journey through the mathematical universe behind these practical successes, elucidating the theoretical underpinnings of deep neural networks in functional analysis, harmonic analysis, complex analysis, approximation theory, dynamical systems, Kolmogorov complexity, optimal transport, and fractal geometry.     

This talk is part of the Isaac Newton Institute Seminar Series series.

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