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## The basics
* Linear algebra: https://www.khanacademy.org/math/linear-algebra
This is a prerequisite for machine learning.
* Machine learning: https://www.coursera.org/learn/machine-learning/
If you decide having a solid knowledge of ML, taking this course will be an efficient way to do so. During this course, the assigned `matlab` homeworks are suggested to be done for a better understanding.
* Deep learning with less material: http://ufldl.stanford.edu/tutorial/
If you already have some basic knowledge of machine learning and desire moving to deep learning earlier, one of a great choices is to follow this tutorial which focuses on only the prerequisite of deep learning. In addition, I also suggest you to finish the related `matlab` assignments.
* Deep learning with rich material: http://deeplearning.net/tutorial/contents.html
This website also teaches the deep learning but with a different programing language `python` for implementation. This place also contains rich details of deep learning structure and relevant theories.
### Others
* Stanford course - CS224d Deep Learning for Natural Language Processing: http://cs224d.stanford.edu/
* Stanford course - CS229 Machine Learning: http://cs229.stanford.edu/
* Stanford course - CS231n Convolutional Neural Networks for Visual Recognition: http://cs231n.stanford.edu/
* VGG model application: http://www.robots.ox.ac.uk/~vgg/practicals/cnn/index.html
