How to learn about deep learning

In this post I will list resources I found useful when I was trying to find out about deep learning. Feel free to add more in the comments.

• Neural Networks for Machine Learning: Hinton's course at Coursera is a wonderful introduction to deep learning. I recommend lectures 11-15 that cover Hopfield nets, Boltzmann machines, sigmoid belief nets and sparse auto-encoders.
• UFLDL tutorial: Tutorial on unsupervised feature learning and deep learning by Andrew Ng. Includes lecture notes and programming exercises. The first programming exercise shows how sparse auto-encoders can self-learn features found in the visual cortex, and the rest shows how these unsupervised features improve performance on supervised visual recognition tasks like MNIST and STL10.
• IPAM graduate summer school on deep learning and feature learning: has video lectures by many of the leaders of the field.
• Torch7: is a Matlab-like environment for state-of-the-art machine learning algorithms used at the IPAM summer school and several research groups on deep learning.
• deeplearning.net lists these and many other resources.