Course Material

• Exercise: Github Repo (open exercises_1.ipynb). Some of you have pointed out a bug in the sample solution (exercises_1_all_code.ipynb) stopping execution just before the dropoutlayer - it have been fixed now.
• Slides: Will be upladed at DTU CampusNet
• Suggested Readings: Christopher M. Bishop “Pattern Recognition and Machine Learning”, chapter 5

Additional Material

• Feedforward NN in Lasagne tutorial How to implement the a feedforward NN in Lasagne.
• Backpropagation explabined by Michael Nielsen(Neural Networks and Deep Learning, chap 2) Chaptor 2 gives a good and pretty elaborate explanation on how backpropagation works
• Neural networks class - Université de Sherbrooke: Neural Network Class by Hugo Larochelle.
• Deep learning at Oxford 2015: Neural Network Class by Nando de Freitas.
• Deep Learning: Recent Deep Learning review paper by Y. LeCun, Y. Bengio and G. Hinton.
• Reducing the Dimensionality of Data with Neural Networks: An influential paper on deeplearning by Hinton and Salakhutdinov that restarted the interest in neural networks.
• Learning Internal Representations by Error Propagation: One of the original Backpropagation papers.