Resources


Tensorflow tutorials

https://colab.research.google.com/github/cs231n/cs231n.github.io/blob/master/python-colab.ipynb

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)
  • Pytorch tutorials (Link)
  • JAX tutorials (Link)
  • Interpretability analysis in Pytorch with Captum (Link)

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)

Deep Learning 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)

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)

Books

DL books

  • Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow (Book, Link)
  • Patterns, Predictions, and Actions: A story about machine learning by Hardt and Recht (Link)
  • Deep Learning by Goodfelloiiiow, Bengio, & Courville (Link)
  • Dive into Deep Learning by Zhang, Lipton, Li & Smola (Link)
  • Interpretable Machine Learning by Christoph Molnar (Link)

Classic ML books

  • Pattern recognition and machine learning by Christopher Bishop (Link, Code, Solutions)
  • Machine Learning: A Probabilistic Interpretation by Kevin Murphy (Link)
  • Information Theory, Inference, and Learning Algorithms by Mackay (Link)
  • Mathematics for Machine Learning by Deisenroth et al. (Link)
  • Understanding Machine Learning by Shalev-Shwartz & Ben-David (Link)

Seminars

  • Machines, Inference and Algorithms series – Broad Institute (Video)
  • DL Paper Reviews by Yannic Kilcher (Video)
  • RNA-COSI Journal Club (Link)

Other Resources

Short Courses

ML/DL

  • Deep Learning Summer School (Jul - Deadline March) (Website)
  • Machine Learning Summer Schools (August - Deadline: April) (Website)
  • Deep Learning Theory Summer School at Princeton (July - Deadline: March) (Website)
  • Deep Learning for Natural Language Processing (2 sessions: summer and winter) (Website)
  • NYU AI School (Jan 4-8 - Deadline: ?) (Website)
  • Janelia ML workshop (March-April - Deadline: Nov): (Website)

Watch previous courses:

  • Deep Learning Summer School (2019) (Video)
  • Machine Learning Summer School 2019 - London (Website, Video)

Articles/Blogs

  • 10 tips for writing CS papers (Link1, Link2)
  • Statistical Significance (Link)