Grant Sanderson

• Grant Sanderson (3Blue1Brown). But what is a neural network? | Chapter 1, Deep learning: 19 minutes. (“What are the neurons, why are there layers, and what is the math underlying it?”)
• Grant Sanderson (3Blue1Brown). Gradient descent, how neural networks learn | Chapter 2, Deep learning: 21 minutes.
• Grant Sanderson (3Blue1Brown). What is backpropagation really doing? | Chapter 3, Deep learning: 14 minutes.
• Grant Sanderson (3Blue1Brown). Backpropagation calculus | Chapter 4, Deep learning: 10 minutes.

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Gitta Kutyniok

• Kutyniok, G. (2022). The Mathematics of Artificial Intelligence. International Congress of Mathematicians 2022. Section “Numerical Analysis and Scientific Computing”. Virtual Event July 6-14, 2022.
• Deep Learning meets Shearlets: Gitta Kutyniok

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Sydney Mathematical Research Institute: Machine Learning for the Working Mathematician

• Basics of Machine Learning: Geordie Williamson
• What can and can’t neural networks do: Joel Gibson
• How to think about machine learning: Georg Gottwald
• Recurrent Neural Nets: Joel Gibson
• Regularization (Machine Learning): Georg Gottwald
• Geometric Deep Learning: Geordie Williamson
• Geometric Deep Learning II: Georg Gottwald
• Saliency + Combinatorial Invariance: Geordie Williamson
• A simple RL setup to find counterexamples to conjectures in mathematics: Adam Zsolt Wagner
• Machine learning for optimizing certain kinds of classification proofs: Carlos Simpson

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Geordie Williamson

Intuition meets AI in pure mathematics: Geordie Williamson DeepMind collaboration

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MIT Introduction to Deep Learning

• Foundations of Deep Learning

• Recurrent Neural Networks and Transformers

• Convolutional Neural Networks
• Deep Generative Modeling
• Reinforcement Learning
• Deep Learning New Frontiers

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