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Lecture 2: Training a Neural Network
Lecture 3: Training (Backpropagation Algorithm)
Lecture 4: Analyzing a network. Using the python framework “keras”
Lecture 5: Image classification
Lecture 6: Convolutional networks, Autoencoder
Lecture 7: Visualization of neuron activations (t-SNE method), Adaptive Gradient Descent Techniques
Lecture 8: Recurrent networks (LSTM)
Lecture 9: Word Vectors, Reinforcement Learning, REINFORCE (Policy Gradient)
Lecture 10: Policy Gradient (continued), Baseline, alphaGo, Q learning
Lecture 11: Q learning (finished), Restricted Boltzmann Machine
Machine Learning for Physicists 