Implementation of a convolutional autoencoder in Pytorch on MNIST dataset.
The Autoencoder contains an encoder and decoder where encoder compresses the images input into a latent space and decoder retrieves back the Images.
Loss function used: MSE Optimizer: Adam optimizer
