- General Introduction to Deep Learning
- Introduction/Applications (slides)
- Programming models for Deep Learning with examples in Theano and TensorFlow
- Introduction into Theano
- Feedforward Neural Networks
- Feedforward Neural Networks in a Nutshell (incl. a numpy implementation)
- Using TensorFlow to build feedforward neural networks:
- Using Theano to build feedforward neural networks:
- Simple Feed Forward Neural Network with Theano
- Slides
- For an implementation of neural networks also work through the first two chapters of the deep learning tutorial (external link)

- Neuron Types
- Autoencoder and Manifold Hypotheses
- Weight initialization
- Optimization:
- Momentum and Nesterov Momentum, Adagrad and Adadelta, 2nd order optimization and Hessian free optimization

- Dropout and Dropconnect

- Recurrent neural networks
- LSTM: Long Short Term Memory
- Deep transition recurrent neural networks
- Stacked recurrent neural networks
- Applications of RNNs

- Intro RBM: Introduction to Restricted Boltzmann Machines
- Gibbs Sampling on RBM
- Contrastive Divergence
- ...
- Renormalization Group and RBMs

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