Other resources

Other study groups

Some topics we might study:

Optimization methods

Introduction, basic unsupervised learning methods (PCA, k-means), empirical risk minimization, standard loss functions, linear classification, stochastic optimizers, hidden layers, deep feedforward networks, backpropagation, regularization ideas, batch normalization

Images

Convolutional networks (CNNs), classifying images, popular architectures, attaining maximal performance in image classification. Deep image generation: generating CNNs, adversarial networks, deep computer vision with feedback loop

Autoencoding

GANs, autoencoders, variational autoencoders, image analogies

Sequences

Recurrent neural networks, deep learning on sequences, deep RNNs, LSTMs, GRUs, deep machine translation

Other reading