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The Mathematical Universe Behind Deep Neural Networks - Rothschild Lecture

Offered By: Isaac Newton Institute for Mathematical Sciences via YouTube

Tags

Deep Neural Networks Courses Machine Learning Courses Complex Analysis Courses Dynamical Systems Courses Functional Analysis Courses Harmonic Analysis Courses Approximation Theory Courses Fractal Geometry Courses Kolmogorov Complexity Courses Optimal Transport Courses

Course Description

Overview

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Embark on a captivating journey through the mathematical universe behind deep neural networks in this Rothschild Lecture delivered by Professor Helmut Bölcskei from ETH Zürich. Explore the theoretical underpinnings of deep neural networks, delving into functional analysis, harmonic analysis, complex analysis, approximation theory, dynamical systems, Kolmogorov complexity, optimal transport, and fractal geometry. Gain insights into the mathematical foundations that have led to breakthrough results in practical machine learning tasks such as image classification, image captioning, control-policy-learning for the board game Go, and protein structure prediction. Discover how these mathematical concepts contribute to the remarkable successes of deep neural networks in various applications.

Syllabus

Date: 10 December 2021 – 16:00 to


Taught by

Isaac Newton Institute for Mathematical Sciences

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