Are you frustrated that introductory Machine Learning courses just aren’t math-heavy, or even rigorous enough? Are you very irritated all the interesting papers with deep theoretical results just don’t get the attention they deserve? Are you looking for juicy rigorous and elegant math heavy content related to Machine Learning?
Well, you have come to the right place.
- Tropical Geometry of Deep Neural Networks by Zhang et al.
- Why does deep and cheap learning work so well? by Lin at al.
- Gradient Descent Finds Global Minima of Deep Neural Networks by Du et al.
- Geometric Understanding of Deep Learning by Lei et al.
- The Value Function Polytope in Reinforcement Learning by Dadashi et al.
- Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville
- Reinforcement Learning: An Introduction by Richard Sutton
- Understanding Machine Learning: From Theory to Algorithms by Shai Shalev-Shwartz and Shai Ben-David
- High-Dimensional Probability by Roman Vershynin
- Linear Algebra and Learning from Data by Gilbert Strang
- Foundations of Machine Learning by David S. Rosenberg @ Bloomberg
- Theories of Deep Learning by Donoho, Monajemi, and Papyan @ Stanford
- Neural Networks, Manifolds, and Topology by Chris Olah