This project is an assignment from the course "Deep Learning". Several things that we learned in this project are:
- Working with PyTorch
- Convolotional Neural Networks
- Working with sequential data
- LSTM Auto-Encoder
- Time Series Forecasting
We worked with three different datasets. Synthetic dataset, MNIST, and S&P500 stock prices.
For the Synthetic dataset, we trained LSTM Auto-Encoder which took as input data (sequential data) from the synthetic dataset, encode this data to a lower dimension and decode the output of the encoder.
For the MNIST dataset, we trained the LSTM Auto-Encoder that took the images of the MNIST dataset and refer to every image as sequential data (row-by-row). Then we added the option of classification to the AE by composing two objective functions. Then we repeat the same thing but instead of referring to every image as a row-by-row sequence we did it pixel-by-pixel.
For the S&P500 dataset, in addition, to training the LSTM AE, we modified the network to perform a prediction of the stock prices.
The AE results on the MNIST dataset:
The AE results on the synthetic dataset:
The AE results on the S&P500 dataset (some specific stock):
