Tuesday 24 September 2019

Machine Learning Best Resources

Machine Learning Interview Questions:-
https://www.analyticsvidhya.com/blog/2018/06/comprehensive-data-science-machine-learning-interview-guide/

Understanding RNN and LSTM's:-
http://colah.github.io/posts/2015-08-Understanding-LSTMs/

Understanding Attention:-
https://distill.pub/2016/augmented-rnns/

Reinforcement Learning:-
http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html

BERT:-
https://mccormickml.com/2019/05/14/BERT-word-embeddings-tutorial/
http://jalammar.github.io/illustrated-bert/
https://jalammar.github.io/

Glue Datasets for Benchmarking:-
https://gluebenchmark.com/tasks/
http://snap.stanford.edu/data/web-Amazon.html

SVM's:-
https://blog.statsbot.co/support-vector-machines-tutorial-c1618e635e93

CNN Cheat Sheet:-
https://github.com/kanha95/books/blob/master/VIP%20Cheatsheet_%20Convolutional%20Neural%20Networks.pdf

Popular Blogs to Follow:-
https://brohrer.github.io/blog.html

Must Watch Short Videos:-
https://www.youtube.com/playlist?list=PLblh5JKOoLUICTaGLRoHQDuF_7q2GfuJF

PROBABILITY:-

https://github.com/kanha95/books/blob/master/Probability%20Cheetsheet.pdf

The above link contains probability cheat sheet which would be helpful for gate and machine learning people. I will update this page as soon as i find good sources like this.

Book:- https://github.com/kanha95/books/blob/master/Ross_8th_ed_English.pdf

Challenging Problems:-
https://github.com/kanha95/books/blob/master/chpr.pdf
https://github.com/kanha95/books/blob/master/fifty_challenging_problems_in__2.pdf
https://github.com/kanha95/books/blob/master/241-Probability.pdf
https://github.com/kanha95/books/blob/master/stats_comp_07_08_q5_soln.pdf