R Deep Learning Cookbook

Data Representation Using Autoencoders

This chapter will introduce unsupervised applications of deep learning using autoencoders. In this chapter, we will cover the following topics:

• Setting up autoencoders
• Data normalization
• Setting up a regularized autoencoder
• Fine-tuning the parameters of the autoencoder
• Setting up stacked autoencoders
• Setting up denoising autoencoders
• Building and comparing stochastic encoders and decoders
• Learning manifolds from autoencoders
• Evaluating the sparse decomposition