An autoencoder network is nowadays one of the widely used deep learning architectures. It's mainly used for unsupervised learning of efficient decoding tasks. It can also be used for dimensionality reduction by learning an encoding or a representation for a specific dataset. Using autoencoders in this chapter, we'll show how to denoise your dataset by constructing another dataset with the same dimensions but less noise. To use this concept in practice, we will extract the important features from the MNIST dataset and try to see how the performance will be significantly enhanced by this.

The following topics will be covered in this chapter:

• Introduction to autoencoders
• Examples of autoencoders
• Autoencoder architectures
• Compressing the MNIST dataset
• Convolutional autoencoders
• Denoising autoencoders
• Applications of autoencoders