Deep Learning



Time and place

Information regarding the online teaching will be added to the studon course

  • Fr 10:15-11:45, Room H4 (exclude vac) ICS

The following lectures are recommended:

  • Introduction to Pattern Recognition (IntroPR)
  • Pattern Recognition (PR)



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.

Additional information

Keywords: deep learning; machine learning

Expected participants: 450

www: https://www.studon.fau.de/crs4449450.html

Friedrich-Alexander-Universität Erlangen-Nürnberg