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

• URL: http://arxiv.org/abs/2112.00851v1
• Date: Sat, 27 Nov 2021 15:12:42 GMT
• Title: The Physics of Machine Learning: An Intuitive Introduction for the Physical Scientist
• Authors: Stephon Alexander, Sarah Bawabe, Batia Friedman-Shaw, Michael W. Toomey
• Abstract summary: This article is intended for physical scientists who wish to gain deeper insights into machine learning algorithms. We begin with a review of two energy-based machine learning algorithms, Hopfield networks and Boltzmann machines, and their connection to the Ising model. We then delve into additional, more "practical," machine learning architectures including feedforward neural networks, convolutional neural networks, and autoencoders.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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