Resources

Useful resources for those starting computational projects in the solab!

Python

• Google colab — Run python notebooks directly in your web-browser! Alternatives: Jupyter by mybinder.org, Anaconda - install on your own computer
• A Whirlwind Tour of Python by J. VanderPlas — Introduction to Basic Python
• Python Data Science Handbook by J. VanderPlas — Introduction to Scientific libraries used in Python

Mathematics and Statistics

• 3Blue1Brown - series of animated youtube videos: Essence of linear algebra, Essence of calculus, Neural Networks, Differential Equations
• Advanced topics: The Elements of Statistical Learning by T. Hastie, R. Tibshirani and J. Friedman; Machine Learning: a Probabilistic Perspective by K. Murphy

Neural networks

• Interactive examples - BEST WAY TO LEARN! Playground (for classification/regression), GAN Lab (generative models), Anomagram (Autoencoder), Distill (interactive publications)
• Deep Learning with Python (not free, link to Amazon) by F. Chollet — BIG picture introduction to NN. Notebooks to go with the book.
• Advanced topics: Deep Learning by I. Goodfellow, Y. Bengio and A. Courville
• Reviews: Deep learning: new computational modeling techniques for genomics; A Survey of Deep Learning for Scientific Discovery

Proteins

• Coevolution - List of milestones
• Papers on machine learning for proteins
• Workshops (by Sergey Ovchinnikov): Abinitio structure prediction (2018), Structure prediction w/ experimental data (2018), Rosetta-Boston-Club (2019) - going from PSSM, MRF to VAE