- Computational Linear Algebra for Coders: https://github.com/fastai/numerical-linear-algebra/blob/master/README.md
- Gilbert Strang's Linear Algebra and Learning from Data book: https://ocw.mit.edu/courses/mathematics/18-065-matrix-methods-in-data-analysis-signal-processing-and-machine-learning-spring-2018/video-lectures/
- Multivariate Calculus: https://www.coursera.org/learn/multivariate-calculus-machine-learning
- Andrew Ng's ML Course: https://www.coursera.org/learn/machine-learning
- FastAI's ML Course: http://course18.fast.ai/ml
- Google's ML Crash Course: https://developers.google.com/machine-learning/crash-course/
- Deep Learning AI Specialization: https://www.coursera.org/specializations/deep-learning
- FastAI Part I: https://course.fast.ai/
- FastAI Part II: http://course18.fast.ai/part2.html
- Full Stack DL: https://fullstackdeeplearning.com/march2019
- Stanford's CS 231n:
- Course Home Page: http://cs231n.github.io/
- Playlist: https://www.youtube.com/playlist?list=PLC1qU-LWwrF64f4QKQT-Vg5Wr4qEE1Zxk
- Stanford's CS 224n:
- Course Home Page: http://web.stanford.edu/class/cs224n/
- Playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z
- FastAI Lectures (when available)
- Coursera TF Specialization:
- Introduction to Tensorflow: https://www.coursera.org/learn/introduction-tensorflow
- CNN's in Tensorflow: https://www.coursera.org/learn/convolutional-neural-networks-tensorflow
- FastAI's Swift for TF lessons:
- Learn Swift:
- Create a small library based on swift for TF for Computer Vision
- Deep Dive into Tensorflow Internals: https://www.youtube.com/watch?v=kVEOCfBy9uY&list=PLOJ0FSJhiKlw9ZokOzxOOnxK6PQuU7YIx
- Official Pytorch tutorial: https://pytorch.org/tutorials/
- Introduction to Deep learning with Pytorch: https://www.udacity.com/course/deep-learning-pytorch--ud188
- To add competitions and code to this list
- Build/Replicate a deep learning library from scratch using FastAI v3 Part II lessons
- Add SOTA methods and algorithms
- Use the library for competing in Kaggle and other contests
- Useful papers to be referenced here with sample code if possible