Moitra. Theoretical Foundations for Deep Learning. MIT
It explores theoretical foundations for deep learning, emphasizing the following themes: (1) Approximation: What sorts of functions can be represented by deep networks, and does depth provably increase the expressive power? (2) Optimization: Essentially all optimization…
- from
- Theoretical Computer Science
- added
- 2026-10-10
- likes
- 0
similar
-
Shalev-Shwartz. Understanding Machine Learning: From Theory to Algorithms cambridge.org
It provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms.
-
MIT 6.S191: Introduction to Deep Learning introtodeeplearning.com
Deep learning foundations and applications.
-
Vazirani & Kearns. An Introduction to Computational Learning Theory mitpress.mit.edu
Emphasizing issues of computational efficiency, It introduces a number of central topics in computational learning theory.
-
On the information bottleneck theory of deep learning artemyk.github.io
Journal of Statistical Mechanics: Theory and Experiment, 2019. [All Versions].
-
Misc from MIT's 'Machine Learning' course ocw.mit.edu
-
Core Concepts of Deep Learning devblogs.nvidia.com
Theoretical Computer Science › Lecture Notes: “It explores theoretical foundations for deep learning, emphasizing the following themes: (1) Approximation: What sorts of functions can be represented by deep networks, and does depth provably increase the expressive power? (2) Optimization: Essentially all optimization…”