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-This is the coding part of the lecture Deep Representation Learning in PyTorch.
-
-python 3.10
-
-#### Autoencoder
-In this demo we will implement a simple autoencoder. The autoencoder will be trained on the MNIST dataset. The autoencoder will be implemented in the file autoencoder.py. The file autoencoder.py contains a class Autoencoder on the MNIST dataset.
-We compare the performance of the fully connected autoencoder with a convolutional autoencoder.
-Jupyter notebooks: 
-* autoencoder_mnist.ipynb
-* autoencoder_cifar10.ipynb
-
-#### Contractive Learning (SimCLR)
-In this demo we implemented the SimClR [1] algorithm and trained it on the cifar10 dataset.
-
-Download the pretrained encoder [here](https://cloud.cps.unileoben.ac.at/index.php/s/feHYqRHwDy7mMDm) and put it in the folder `runs`.
-
-The code was adapted from this [repo](https://github.com/sthalles/SimCLR/tree/master)
-
-Jupyter notebooks:
-* simclr.ipynb
-
-
-[1] Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020. A simple framework for contrastive learning of visual representations. In Proceedings of the 37th International Conference on Machine Learning (ICML'20), Vol. 119. JMLR.org, Article 149, 1597–1607.
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