The Physics of Machine Learning: An Intuitive Introduction for the Physical Scientist
It provides an intuitive introduction for physical scientists to understand machine learning through familiar physics concepts, but it is incremental as it repackages existing knowledge without new research contributions.
The paper introduces machine learning concepts to physical scientists by connecting energy-based algorithms like Hopfield networks and Boltzmann machines to the Ising model, and then covers practical architectures such as feedforward neural networks and convolutional neural networks with code examples.
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.