A high-bias, low-variance introduction to Machine Learning for physicists
Pankaj Mehta, Marin Bukov, Ching-Hao Wang, Alexandre G.R. Day, Clint Richardson, Charles K. Fisher, David J. Schwab
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A high-bias, low-variance introduction to Machine Learning for physicists presents a ising model approach for python (programming language).
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Utility signals: depth 55/100, grounding 58/100, status medium.
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- Optax Adam optimizer docs
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TensorFlow/Keras baseline for Adam.
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Research context
897
Citations
159
References
Tasks
Python (programming language), Unsupervised learning, Cluster analysis, Boltzmann machine, Artificial neural network, Gradient descent, Supervised learning, Computer science
Methods
Ising model, Algorithmic learning theory, Model selection
Domains
Artificial intelligence, Machine learning, Physics
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