| autoencoder_cifar10.ipynb | ||
| autoencoder_mnist.ipynb | ||
| conv_animation.ipynb | ||
| CPS_Database_Basics.ipynb | ||
| CPS_Python_Basics.ipynb | ||
| data_exploration.ipynb | ||
| evaluation_results.log | ||
| Machine Learning Models .pdf | ||
| MLBook.pdf | ||
| normalized_test_data.csv | ||
| normalized_train_data.csv | ||
| README.md | ||
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 and put it in the folder runs.
The code was adapted from this repo
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.