Resources

Great Posts

1. An intro to neural nets (builds up from small pieces): http://neuralnetworksanddeeplearning.com/chap1.html
   • Some alternatives are:
     • https://www.youtube.com/watch?v=aircAruvnKk
   • https://ujjwalkarn.me/2016/08/09/quick-intro-neural-networks/
   • https://serokell.io/blog/deep-learning-and-neural-network-guide
2. Deep-dive on gradient descent algorithms (the first section is most important, the visualizations later on are nice): https://www.ruder.io/optimizing-gradient-descent/
3. Overview of backpropagation: https://colah.github.io/posts/2015-08-Backprop/

Blogs

• https://lilianweng.github.io/
• https://dennybritz.com/
• https://ruder.io/
• https://timdettmers.com/
• https://colah.github.io/
• https://karpathy.github.io
• https://distill.pub

More posts

• https://kidger.site/thoughts/just-know-stuff/
• https://jalammar.github.io/illustrated-transformer/

Books

The following books are all available online for free:

• Probabilistic Machine Learning: An introduction by Kevin Murphy: https://probml.github.io/pml-book/book1.html
• Deep Learning by Ian Goodfellow and others: https://www.deeplearningbook.org/ or the pdf version
• Mathematics for Machine Learning by Marc Deisenroth and others: https://mml-book.github.io/, pdf version

Videos

• https://www.youtube.com/watch?v=aircAruvnKk

We are constantly adding to this list, so if you have any suggestions, please let us know!