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)

Kernel density estimation (KDE)

The solution to the problem of the discontinuity of histograms can be effectively addressed with a simple method. Given a sample xi ∈ X, it's possible to consider a hypervolume (normally a hypercube or a hypersphere), assuming that we are working with multivariate distributions, whose center is xi. The extension of such a region is defined through a constant h called bandwidth (the name has been chosen to support the meaning of a limited area where the value is positive). However, instead of simply counting the number of samples belonging to the hypervolume, we now approximate this value using a smooth kernel function K(xi; h) with some important features:

Moreover, for statistical and practical reasons, it's also necessary to enforce the following integral constraints (for simplicity, they are shown only for a univariate case...