Deep Learning 2019/2020

Detailed information

Keywords: introduction batch local pattern perceptron analytic recognition optimization learning neural entropy functions subgradients regression gradient function network descent problems iteration

Most recent entry on 2019-11-05 

Faculty

Lehrstuhl für Informatik 5 (Mustererkennung)

Recording type

Vorlesungsreihe

Via

Free

Language

English

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.

Associated Clips

Episode
Title
Lecturer
Updated
Via
Duration
Media
1
Deep Learning
Dipl.-Inf. Vincent Christlein
2019-10-15
Free
01:08:08
2
Deep Learning
Prof. Dr. Andreas Maier
2019-10-29
Free
01:24:10
3
Deep Learning
M. Sc. Katharina Breininger
2019-11-05
Free
01:23:24

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