Book Image

Hands-On Unsupervised Learning with Python

By : Giuseppe Bonaccorso
Book Image

Hands-On Unsupervised Learning with Python

By: Giuseppe Bonaccorso

Overview of this book

Unsupervised learning is about making use of raw, untagged data and applying learning algorithms to it to help a machine predict its outcome. With this book, you will explore the concept of unsupervised learning to cluster large sets of data and analyze them repeatedly until the desired outcome is found using Python. This book starts with the key differences between supervised, unsupervised, and semi-supervised learning. You will be introduced to the best-used libraries and frameworks from the Python ecosystem and address unsupervised learning in both the machine learning and deep learning domains. You will explore various algorithms, techniques that are used to implement unsupervised learning in real-world use cases. You will learn a variety of unsupervised learning approaches, including randomized optimization, clustering, feature selection and transformation, and information theory. You will get hands-on experience with how neural networks can be employed in unsupervised scenarios. You will also explore the steps involved in building and training a GAN in order to process images. By the end of this book, you will have learned the art of unsupervised learning for different real-world challenges.
Table of Contents (12 chapters)

Probability density functions

In all previous chapters, we have always supposed that our datasets were drawn from an implicit data-generating process pdata and all the algorithms assumed xi ∈ X as independent and identically distributed (IID) and uniformly sampled. We were supposing that X represented pdata with enough accuracy so that an algorithm could learn to generalize with limited initial knowledge. In this chapter, instead, we are interested in directly modeling pdata without any specific restriction (for example, a Gaussian mixture model achieves this goal by imposing a constraint on the structure of the distributions). Before discussing some very powerful approaches, it's helpful to briefly recap the properties of a generic continuous probability density function p(x) defined on a measurable subset X ℜn (to avoid confusion, we are going to indicate...