Unsupervised Learning

This chapter delves into unsupervised learning models. In the previous chapter we explored Autoencoders, novel neural networks that learn via unsupervised learning. In this chapter we will delve deeper into some other unsupervised learning models. In contrast to supervised learning, where the training dataset consists of both the input and the desired labels, unsupervised learning deals with the case where the model is provided only the input. The model learns the inherent input distribution by itself without any desired label guiding it. Clustering and dimensionality reduction are the two most commonly used unsupervised learning techniques. In this chapter we will learn about different machine learning and NN techniques for both. We will cover techniques required for clustering and dimensionality reduction, and will go into the details of Boltzmann machines, and finally we will cover the implementation of the aforementioned techniques using TensorFlow. The...