Deep Learning 2019/2020 (QHD 1920 - Video & Folien)

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.