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)

Gaussian mixture

Gaussian mixture is one of the most well-known soft clustering approaches, with dozens of specific applications. It can be considered the father of k-means, because the way it works is very similar; but, contrary to that algorithm, given a sample xi ∈ X and k clusters (which are represented as Gaussian distributions), it provides a probability vector, [p(xi ∈ C1), ..., p(xi ∈ Ck)].

In a more general way, if the dataset, X, has been sampled from a data-generating process, pdata, a Gaussian mixture model is based on the following assumption:

In other words, the data-generating process is approximated by the weighted sum of multivariate Gaussian distributions. The probability density function of such a distribution is as follows:

The influence of each component of every multivariate Gaussian depends on the structure of the covariance matrix...