# Resources

There are many online resources that can be helpful for learning the content of this class. Here are a few. If you find others, please post on Piazza and we can add them here.

- Michael Nielsenâ€™s online book
- Deep Learning textbook by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
- Unsupervised Feature Learning and Deep Learning Tutorial from Stanford
- CS231n: Convolutional Neural Networks for Visual Recognition lecture notes by Andrej Karpathy
- CS294: Deep Reinforcement Learning Course on reinforcement learning by Sergey Levine

Useful Links

Part I

- An overview of gradient descent optimization algorithms
- Convolutional Neural Networks backpropagation: from intuition to derivation
- A guide to receptive field arithmetic for Convolutional Neural Networks

Part II

- Autoencoders
- Vector Representations of Words
- Backpropagation Through Time and Vanishing Gradients
- Understanding LSTM Networks
- David Silver, Google DeepMind: Deep Reinforcement Learning

Extra readings on the history of neural networks and their recent revival:

- Nature article by Yann LeCun, Yoshua Bengio, and Geoffrey Hinton
- Technical report by Jurgen Schmidhuber