Possibly-Useful-Links

http://web.stanford.edu/class/cme241/

https://machinelearningmastery.com/maximum-a-posteriori-estimation/

https://www.slideshare.net/cover_drive/realworld-derivatives-hedging-with-deep-rl

https://www.analyticsvidhya.com/blog/2018/09/an-end-to-end-guide-to-understand-the-math-behind-xgboost/

https://machinelearningmastery.com/cross-entropy-for-machine-learning/

https://github.com/udacity/deep-reinforcement-learning

https://deepmind.com/

https://www.tensorflow.org/guide/effective_tf2

http://aikorea.org/awesome-rl

https://jfds.iijournals.com/

https://ai.google/

https://twitter.com/deepmindai?lang=en

https://machinelearningmastery.com/

https://hackernoon.com/

https://github.com/google/dopamine

https://www.programcreek.com/python/example/82598/sklearn.metrics.auc

https://github.com/lytemar/Applied-Machine-Learning-in-Python--University-of-Michigan---Coursera

https://scikit-plot.readthedocs.io/en/stable/

https://www.deeplearning.ai/

http://www.kaggle.com

http://archive.ics.uci.edu/ml/index.php

pd.options.display.max_seq_items = 2000

https://www.tensorflow.org/community/roadmap

https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1492032646/ref=mp_s_a_1_4?keywords=hands+on+machine+learning+with+scikit-learn+and+tensorflow&qid=1568138620&s=gateway&sprefix=hands+&sr=8-4