Resources for Quantitative Scientists


Tensorflow tutorials

Recommended:

  • Tensorflow tutorials (Link)
  • Francis Chollet’s tensorflow 2 + keras tutorial (Code)
  • Interpretability analysis with Tensorflow 2.0 (Link)

Other resources:

  • Coursera TensorFlow 2 for Deep Learning Specialization by Imperial College London (Video)

Machine Learning

Courses

Recommended:

  • Andrew Ng’s Intro to Machine Learning Course at Stanford (Website, Video)

Other resources:

  • Yaser Abu-Mostafa’s Learning from Data Course at Caltech (Website, Video)
  • David Mackay’s Information Theory, Pattern Recognition, and Neural Networks Course at Cambridge (Video, Book)

Books

Recommended:

  • Mathematics for Machine Learning by Deisenroth et al. (Link)
  • Pattern recognition and machine learning by Christopher Bishop (Link, Code, Solutions)

Other resources:

  • Machine Learning: A Probabilistic Interpretation by Kevin Murphy (Link)
  • Information Theory, Inference, and Learning Algorithms by Mackay (Link)
  • Understanding Machine Learning by Shalev-Shwartz & Ben-David (Link)

Deep Learning courses

Courses

Recommended:

  • Convolutional Neural Networks for Visual Recognition Course at Stanford (Website, Video)
  • Designing, Visualizing and Understanding Deep Neural Networks at UC Berkeley by Sergey Levine (Website, Video)

Additional resources:

  • Pieter Abeel’s Deep Unsupervised learning Course at Berkeley (Website, Video)
  • Natural Language Processing with Deep Learning Course at Stanford (Website, Video)
  • Deep Multi-Task and Meta Learning Course at Stanford (Website, Video)
  • Yann LeCunn’s deep learning course at NYU (Website, Video)
  • Berkeley’s Intro to Deep Learning (Stat 157) (Website, Video)

Books

Recommended:

  • Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow (Book, Link)

Additional resources:

  • Patterns, Predictions, and Actions: A story about machine learning by Hardt and Recht (Link)
  • Deep Learning by Goodfellow, Bengio, & Courville (Link)
  • Dive into Deep Learning by Zhang, Lipton, Li & Smola (Link)
  • Interpretable Machine Learning by Christoph Molnar (Link)

Biology courses

Recommended:

  • Shirley Liu’s Introduction to Computational Biology and bioinformatics Course at Harvard (Website, Video)

Other resources:

  • Manolis Kellis’ Deep Learning in the Life Sciences Course at MIT (Website, Video)
  • Eric Lander’s Intro to Biology Course at MIT (Video)
  • Foundations of Computational and Systems Biology Course at MIT (Website, Video)