Mathematical Principles of Deep Learning

Content will be added throughout fall 2022. Additional (free and accessible) resources:

• Lecture notes by Matus Telgarsky

• Lecture notes by Tengyu Ma

• Handwritten notes for a Course on the Mathematics of Deep Learning by Joan Bruna

• A video recording of a presentation by Tyrus Berry, which touches on a lot of topics in data science and machine learning in an intuitive way

• A high-bias, low-variance introduction to Machine Learning with associated Python notebooks (which can be run online)

• Google Colab is a free online Python environment in which you can easily run code with the most common libraries for machine learning. It supports GPU processing, but will limit your processing power if your use of computing resources becomes very high.

• There are several recent overview articles over the mathematical theory of deep learning:

  • Higham and Higham (2018)

  • E, Ma, Wojtowytsch and Wu (2020)

  • Berner, Grohs, Kutyniok and Petersen (2021)

• A textbook on deep learning (with a focus for non-mathematicians)

• An introduction by Roberts, Yaida and Hanin (2021)

I am grateful for further sources or amterials. Please email me with suggestions for links to add to the list.