MIT Compbio Lecture 12 - Deep Learning (Fall ’19)

1. Supervised Learning with Neural networks - Perceptron, layers, activation units (sigmoid, softplus, ReLU) - Learning: Gradient, Back-propagation, Rate, Dropout, Overfitting 2. Unsupervised learning with Deep belief networks & autoencoders - Boltzmann machines, Restricted BMs (RBMs), Deep belief networks - Learning: Energy, Gibbs Sampling, Simulated Annealing, Wake-sleep 3. Modern deep learning architectures - Auto-encoders: Self-training, representation learning, RBM pre-training - Convolutional neural n