Deep Learning

Friedrich-Alexander-Universität Erlangen-Nürnberg
LEVEL
Master
TYPE
Course
MODES
-
LANGUAGE
-
ECTS
25
PERIOD
25/04/2022 to 29/07/2022

Course Description

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.

Subject area

Digital communications IA electronics

Field area

Digital
Industry and Space

Educational-info

ECTS

25

Organizer

Partner

Friedrich-Alexander-Universität Erlangen-Nürnberg

Faculty

Faculty of Engineering

Department

Computer Science

Contact or registration links