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Deep Learning - Plain Version
By Prof. Dr.-Ing. Andreas MaierEducation
Deep Learning - Plain Version episodes
- 11 min
- 25 min
- 17 min
- 11 min
- 12 min
- 8 min
- Deep Learning - Graph Deep Learning Part 1
Deep Learning - Graph Deep Learning Part 1
In this video, we introduce spectral representations of graphs.
Further Reading:
A gentle Introduction to Deep Learning12 min - Deep Learning - Graph Deep Learning Part 2
Deep Learning - Graph Deep Learning Part 2
In this video, we demonstrate how to go from spectral to spatial domain in graphs.
Further Reading:
A gentle Introduction to Deep LearningReferences
[1]: Kipf, Thomas N., and Max Welling. "Semi-supervised classification with graph convolutional networks." arXiv preprint arXiv:1609.02907 (2016).
[2]: Hamilton, Will, Zhitao Ying, and Jure Leskovec. "Inductive representation learning on large graphs." Advances in neural information processing systems. 2017.[3]: Wolterink, Jelmer M., Tim Leiner, and Ivana Išgum. "Graph convolutional networks for coronary artery segmentation in cardiac CT angiography." International Workshop on Graph Learning in Medical Imaging. Springer, Cham, 2019.[4]: Wu, Zonghan, et al. "A comprehensive survey on graph neural networks." arXiv preprint arXiv:1901.00596 (2019).[5]: Bronstein, Michael et al. Lecture “Geometric deep learning on graphs and manifolds” held at SIAM Tutorial Portlan (2018)Image References
[a] https://de.serlo.org/mathe/funktionen/funktionsbegriff/funktionen-graphen/graph-funktion
[b] https://www.nwrfc.noaa.gov/snow/plot_SWE.php?id=AFSW1[c] https://tennisbeiolympia.wordpress.com/meilensteine/steffi-graf/[d] https://www.pinterest.de/pin/624381935818627852/[e] https://www.uihere.com/free-cliparts/the-pentagon-pentagram-symbol-regular-polygon-golden-five-pointed-star-2282605[f] http://geometricdeeplearning.com/ (Geometric Deep Learning on Graphs and Manifolds)[g] https://i.stack.imgur.com/NU7y2.png[h] https://de.wikipedia.org/wiki/Datei:Convolution_Animation_(Gaussian).gif[i]https://www.researchgate.net/publication/306293638/figure/fig1/AS:396934507450372@1471647969381/Example-of-centerline- extracted-left-and-coronary-artery-tree-mesh-reconstruction.png[j] https://www.eurorad.org/sites/default/files/styles/figure_image_teaser_large/public/figure_image/2018-08/0000015888/000006.jpg?itok=hwX1sbCO11 min - Deep Learning - Known Operator Learning Part 1
Deep Learning - Known Operator Learning Part 1
In this video, we ask ourselves whether we can find means to make deep learning a little safer, e.g. for medical applications.
Further Reading:
A gentle Introduction to Deep Learning8 min - Deep Learning - Known Operator Learning Part 2
Deep Learning - Known Operator Learning Part 2
In this video, we show the theoretical foundations of known operator learning and how to derive maximal error bounds.
Further Reading:
A gentle Introduction to Deep Learning11 min
About Deep Learning - Plain Version
From the publisher's feed
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.
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