Guide to Machine Learning with Geometric, Topological, and Algebraic Structures (arxiv.org)

Screenshot of arxiv.org: Guide to Machine Learning with Geometric, Topological, and Algebraic Structures

First screen, captured 2 years ago.

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Screenshot of arxiv.org: Matrix Calculus (For Machine Learning and Beyond)

Matrix Calculus (For Machine Learning and Beyond)(arxiv.org)

160 points by ibobev 1 year ago | 27 comments

Screenshot of arxiv.org: Matryoshka Representation Learning

Matryoshka Representation Learning (arxiv.org)

71 points by fzliu 2 years ago | 10 comments

Screenshot of arxiv.org: Parametric Matrix Models

Parametric Matrix Models (arxiv.org)

50 points by evanb 2 years ago | 5 comments

Screenshot of arxiv.org: Every model learned by gradient descent is approximately a kernel machine (2020)

Every model learned by gradient descent is approximately a kernel machine (2020)(arxiv.org)

157 points by Anon84 2 years ago | 111 comments

Screenshot of arxiv.org: Learning Universal Predictors

Learning Universal Predictors (arxiv.org)

52 points by jandrewrogers 2 years ago | 14 comments

Screenshot of arxiv.org: Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory

Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory (arxiv.org)

412 points by Anon84 2 years ago | 132 comments

arXiv listing for a book by Jentzen, Kuckuck, and von Wurstemberger giving a mathematically rigorous introduction to deep learning methods, implementations, and theory.

deep-learning neural-networks optimization mathematics

Looks similar

Matrix Calculus (For Machine Learning and Beyond)(arxiv.org)

Matryoshka Representation Learning(arxiv.org)

Parametric Matrix Models(arxiv.org)

Every model learned by gradient descent is approximately a kernel machine (2020)(arxiv.org)

Learning Universal Predictors(arxiv.org)

Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory(arxiv.org)

Matryoshka Representation Learning (arxiv.org)

Parametric Matrix Models (arxiv.org)

Learning Universal Predictors (arxiv.org)

Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory (arxiv.org)