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)

Mean shift

Let's consider having a dataset X ∈ ℜM × N (M N-dimensional samples) drawn from a multivariate data generating process pdata. The goal of the mean shift algorithm applied to a clustering problem is to find the regions where pdata is maximum and associate the samples contained in a surrounding subregion to the same cluster. As pdata is a Probability Density Function (PDF), it is reasonable for representing it as the sum of regular PDFs (for example, Gaussians) characterized by a small subset of parameters, such as mean and variance. In this way, a sample can be supposed to be generated by the PDF with the highest probability. We are going to discuss this process also in Chapter 5, Soft Clustering and Gaussian Mixture Models, and Chapter 6, Anomaly Detection. For our purposes, it's helpful to restructure the problem as an iterative procedure...