  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.
