Chapter 12. Learning Features with Unsupervised Generative Networks
This chapter focuses on a new type of model, the generative models, which include Restricted Boltzmann Machines, Deep Belief Networks, Variational Auto Encoders, Autoregressive models, and Generative Adversarial Networks. For the first nets, we've limited the presentation to the theory, while the last is explained in detail with practical code and advice.
These nets do not require any labels to be trained, which is called unsupervised learning. Unsupervised learning helps compute features from the data, without the bias of the labels. These models are generative in the sense that they are trained to generate new data that sounds real.
The following points will be covered:
- Generative models
- Unsupervised learning
- Restricted Boltzmann Machines
- Deep belief networks
- Generative adversarial models
- Semi-supervised learning
