ZuzooVn
commented
7 years ago I received comments from Hacker News's user adamnemecek (He's also on Github: @adamnemecek) for this roadmap.
To be honest, this isn't the best list, it's a bit too blog heavy. I've started reading up on ML only recently but here are my recommendations. Note that I haven't went through all of them in entirety but they all seem useful. Note that a lot of them overlap to a large degree and that this list is more of a "choose your own adventure" than "you have to read all of these". Reqs:
- Metacademy (http://metacademy.org) If you just want to check out what ML is about this is the best site.
- Better Explained (https://betterexplained.com/) if you need to brush up on some of the math
- Introduction to Probability (https://smile.amazon.com/Introduction-Probability-Chapman-St...)
- Stanford EE263: Introduction to Linear Dynamical Systems (http://ee263.stanford.edu/) Beginner:
- Andrew Ng's class (http://cs229.stanford.edu)
- Python Machine Learning (https://smile.amazon.com/Python-Machine-Learning-Sebastian-R...)
- An Introduction to Statistical Learning (https://smile.amazon.com/Introduction-Statistical-Learning-A...) Intermediate:
- Pattern Recognition and Machine Learning (https://smile.amazon.com/Pattern-Recognition-Learning-Inform...)
- Machine Learning: A Probabilistic Perspective (https://smile.amazon.com/Machine-Learning-Probabilistic-Pers...)
- All of Statistics: A Concise Course in Statistical Inference (https://smile.amazon.com/gp/product/0387402721/)
- Elements of Statistical Learning: Data Mining, Inference, and Prediction (https://smile.amazon.com/gp/product/0387848576(
- Stanford CS131 Computer vision (http://vision.stanford.edu/teaching/cs131_fall1617/)
- Stanford CS231n Convolutional Neural Networks for Visual Recognition (http://cs231n.github.io/)
- Convex Optimization (https://smile.amazon.com/Convex-Optimization-Stephen-Boyd/dp...)
- Deep Learning (http://www.deeplearningbook.org/ or https://smile.amazon.com/Deep-Learning-Adaptive-Computation-...)
- Neural Networks and Deep Learning (http://neuralnetworksanddeeplearning.com/) Advanced:
- Probabilistic Graphical Models: Principles and Techniques (https://smile.amazon.com/Probabilistic-Graphical-Models-Prin...) I have also found that looking into probabilistic programming is helpful too. These resources are pretty good:
- The Design and Implementation of Probabilistic Programming Languages (http://dippl.org)
- Practical Probabilistic Programming (https://smile.amazon.com/Practical-Probabilistic-Programming...) The currently most popular ML frameworks are scikit-learn, Tensorflow, Theano and Keras.
From the near future machine learning developer's point of view, this roadmap can be divided into some groups: Newbie, Explorer, Adventurer, Trailblazer
At the moment, I think all of the resources here are belong to "Newbie". Currently, I have no ideas about A little bit more advanced roadway with math and theory.
Pull requests are always welcome.