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.