Machine Learning
Overview
Topics in:
NN, CNN, RNN, LSTM, Reinforcement, GAN
Tools and libraries:
pyplotlib, scipy, numpy, tensorflow
Ask HN: Full-on machine learning for 2020, what are the best resources? | Hacker News
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Honestly, skip all of the courses. Pick a problem to solve, start googling for common models that are used to solve the problem, then go on github, find code that solves that problem or a similar one. Download the code and start working with it, change it, experiment. All of the theory and such is mostly worthless, its too much to learn from scratch and you will probably use very little of it. There is so much ml code on github to learn from, its really the best way. When you encounter a concept you need to understand, google the concept and learn the background info. This will give you a highly applied and intuitive understanding of solving ml problems, but you will have large gaps. Which is fine, unless you are going in for job interviews.
References
Papers with Code - The latest in Machine Learning
Machine Learning Glossary | Google Developers
Links & Tutorials
Forwardpropagation - ML Glossary documentation
https://www.youtube.com/watch?v=NfnWJUyUJYU&list=PLkt2uSq6rBVctENoVBg1TpCC7OQi31AlC
CS231n Convolutional Neural Networks for Visual Recognition
Mathematics for Machine Learning
Mathematics for Machine Learning
Stanford CS321n
CS231n Convolutional Neural Networks for Visual Recognition