Deep Learning (DL) has attracted much interest in a wide range of applications such as image recognition, speech recognition and artificial intelligence, both from academia and industry. This lecture introduces the core elements of neural networks and deep learning, it comprises:


(multilayer) perceptron, backpropagation, fully connected neural networks


loss functions and optimization strategies


convolutional neural networks (CNNs)


activation functions


regularization strategies


common practices for training and evaluating neural networks


visualization of networks and results


common architectures, such as LeNet, Alexnet, VGG, GoogleNet


recurrent neural networks (RNN, TBPTT, LSTM, GRU)


deep reinforcement learning


unsupervised learning (autoencoder, RBM, DBM, VAE)


generative adversarial networks (GANs)


weakly supervised learning


applications of deep learning (segmentation, object detection, speech recognition, ...)



The accompanying exercises will provide a deeper understanding of the workings and architecture of neural networks.