Please add cool ML Links here
https://see.stanford.edu/materials/aimlcs229/ps1_solution.pdf http://cs229.stanford.edu/syllabus.html http://cs229.stanford.edu/notes2019fall/cs229-notes1.pdf http://cs229.stanford.edu/section/cs229-linalg.pdf
https://www.youtube.com/watch?v=HZ4cvaztQEs&list=PLA89DCFA6ADACE599&index=3
https://github.com/JuliaDocs/Julia-Cheat-Sheet
https://www.youtube.com/watch?v=oJNHXPs0XDk
https://teddykoker.com/2019/12/beating-the-odds-machine-learning-for-horse-racing/
All the content in this URL is collected by Open Source Society University (OSSU)
https://github.com/ossu/data-science
https://www.youtube.com/watch?v=Gv9_4yMHFhI&list=PLblh5JKOoLUICTaGLRoHQDuF_7q2GfuJF
To learn maths behind ML algorithms and understand concept https://www.ime.unicamp.br/~dias/Intoduction%20to%20Statistical%20Learning.pdf
https://medium.com/intuitionmachine/should-deep-learning-use-complex-numbers-edbd3aac3fb8
https://github.com/dair-ai/nlp_newsletter
Bert stuff https://jalammar.github.io/visualizing-neural-machine-translation-mechanics-of-seq2seq-models-with-attention/ https://jalammar.github.io/
https://jalammar.github.io/illustrated-bert/ https://jalammar.github.io/illustrated-transformer/
https://medium.com/@infoecho/dcnet-denoising-dna-sequence-with-a-lstm-rnn-and-pytorch-3b454ff727e7
https://nlp.seas.harvard.edu/2018/04/03/attention.html
https://github.com/google-research/bert/commits?author=jacobdevlin-google https://github.com/google-research/bert
https://www.blog.google/products/search/search-language-understanding-bert