# The Physics of Machine Learning: An Intuitive Introduction for the Physical Scientist

arXiv: https://arxiv.org/abs/2112.00851 · HTML: https://mlsanity.com/paper/2112.00851/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 33 references that are in the map.
- First posted on arXiv 27 Nov 2021.

## Builds on (most cited first, 5 of 5)

- [Gauge Equivariant Convolutional Networks and the Icosahedral CNN](https://mlsanity.com/paper/1902.04615/) (2019)
- [The Principles of Deep Learning Theory](https://mlsanity.com/paper/2106.10165/) (2021)
- [An exact mapping between the Variational Renormalization Group and Deep Learning](https://mlsanity.com/paper/1410.3831/) (2014)
- [The Autodidactic Universe](https://mlsanity.com/paper/2104.03902/) (2021)
- [Identifying microlensing events using neural networks](https://mlsanity.com/paper/2008.11930/) (2020)

## Abstract (arXiv)

This article is intended for physical scientists who wish to gain deeper insights into machine learning algorithms which we present via the domain they know best, physics. We begin with a review of two energy-based machine learning algorithms, Hopfield networks and Boltzmann machines, and their connection to the Ising model. This serves as a foundation to understand the phenomenon of learning more generally. Equipped with this intuition we then delve into additional, more "practical," machine learning architectures including feedforward neural networks, convolutional neural networks, and autoencoders. We also provide code that explicitly demonstrates training a neural network with gradient descent.