Deep Learning with TensorFlow

Summary

In this chapter, we have implemented some optimizing networks, called autoencoders. An autoencoder is basically a data-compression network model.

It is used to encode a given input into a representation of a smaller dimension; then, a decoder can be used to reconstruct the input back from the encoded version. All the autoencoders we implemented contain an encoding, and a decoding, part.

We have also looked at how to improve the autoencoder's performance, introducing a noise during network training, and building a denoising autoencoder. Finally, we applied the concepts of the CNN networks introduced in Chapter 4, TensorFlow on a Convolutional Neural Network, with the implementation of convolutional autoencoders.

In the next chapter, ...