Copy of Physics/Machine Learning Articles — Marco Tavora Ph.D.

Physics/Machine Learning

Articles about the Intersections Between Machine Learning and Physics

Brief Descriptions

In theses articles, published on Towards Data Science and freeCodeCamp I discuss several topics including:

How to understand the effectiveness of machine learning optimization algorithms, such as stochastic gradient descent, from the perspective of theoretical physics (link)
The connections between the well-known Kolmogorov-Arnold theorem, from real analysis, and the impressive generalization power of artificial neural networks (link)
The reasons why artificial neural networks can predict the outcomes of almost any process in nature (link).
How restricted Boltzmann machines (RBMs), building blocks of deep neural networks, can be used to compute the state of lowest energy of certain kinds of quantum systems (link).
A detailed description of the interconnections between deep learning and renormalization group theory (link).

Copy of The Approximation Power of Neural Network

Copy of The Approximation Power of Neural Network

Copy of Connections between Neural Networks and Pure Mathematics

Copy of Connections between Neural Networks and Pure Mathematics

Copy of Machine Learning and Particle Motion in Liquids: An Elegant Link

Copy of Machine Learning and Particle Motion in Liquids: An Elegant Link

Copy of Neural Quantum States

Copy of Neural Quantum States

Copy of Deep Learning Explainability: Hints from Physics

Copy of Deep Learning Explainability: Hints from Physics

Copy of Article Index

Copy of Article Index