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)