Resources

Python

• Google Colab — run Python notebooks in your browser. Alternatives: Jupyter (mybinder.org) or Anaconda for a local install.
• A Whirlwind Tour of Python — J. VanderPlas · introduction to basic Python.
• Python Data Science Handbook — J. VanderPlas · the scientific Python libraries.

Mathematics & Statistics

• 3Blue1Brown — animated videos on linear algebra, calculus, neural networks and differential equations.
• The Elements of Statistical Learning — Hastie, Tibshirani & Friedman.
• Machine Learning: a Probabilistic Perspective — K. Murphy.

Neural Networks

• TensorFlow Playground — the best way to build intuition. More interactive demos: GAN Lab (generative), Anomagram (autoencoders), Distill (interactive papers).
• Deep Learning with Python — F. Chollet · big-picture intro (paid), with companion notebooks.
• Deep Learning — Goodfellow, Bengio & Courville · the advanced reference.
• Reviews: deep learning for genomics and a survey for scientific discovery.

Proteins

• Coevolution — a list of milestones.
• Machine learning for proteins — a curated list of papers.
• Workshops (S. Ovchinnikov): ab initio structure prediction (2018), structure prediction with experimental data (2018), and Rosetta-Boston-Club (2019).