Aufnahme Datum 2018-12-04

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Kurs-Verknüpfung

Deep Learning

Zugang

Frei

Sprache

Englisch

Fakultät

Lehrstuhl für Informatik 5 (Mustererkennung)

Produzent

MultiMediaZentrum

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, ...)

Nächstes Video

Maier, Andreas
Prof. Dr. Andreas Maier
2018-12-12
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Maier, Andreas
Prof. Dr. Andreas Maier
2018-12-18
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Maier, Andreas
MA Katharina Breininger
2019-01-09
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Maier, Andreas
Prof. Dr. Andreas Maier
2019-01-22
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Maier, Andreas
Prof. Dr. Andreas Maier
2019-01-29
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