Machine Learning


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

Since the “How Do I Learn AI/ML” question pops up on Hacker News once a month (m… | Hacker News

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.


Papers with Code - The latest in Machine Learning

Towards Data Science

Machine Learning Glossary | Google Developers

Forwardpropagation - ML Glossary documentation

CS231n Convolutional Neural Networks for Visual Recognition

Mathematics for Machine Learning

Mathematics for Machine Learning

Stanford CS321n

CS231n Convolutional Neural Networks for Visual Recognition

Generative Adversarial Networks

Understanding Generative Adversarial Networks (GANs)