Autoencoders are unsupervised artificial neural networks for which the input is same as the output.They work by compressing the input into a latent representation and then reconstructing the output from this representation.

This notebook shows the implementation of following autoencoders:

• Fully connected autoencoder
• Sparse autoencoder
• Convolutional autoencoder
• Denoising (convolutional) autoencoder
• Sequence-to-sequence autoencoder
• Variational autoencoder

This notebook also covers some interesting practical applications of autoencoders such as data denoising and dimensionality reduction for data visualisation.

input and output