In this chapter, we will explain what Deep Belief Networks (DBNs) are and how they have been applied to some real-world problems. We will initially introduce some fundamental concepts that need to be understood first, before diving into the details of DBNs. We will give examples as to how these models can be implemented in Python and make predictions on some commonly used datasets.
The following are the topics covered in this chapter:
- Overview of DBNs
- DBN architecture
- Training DBNs
- Fine-tuning
- Datasets and libraries
