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)

Summary

In this chapter, we discussed some quite common neural models that are employed for solving unsupervised tasks. Autoencoders allow you to find the low-dimensional representation of a dataset without specific limits to its complexity. In particular, the use of deep convolutional networks helps to detect and learn both high-level and low-level geometrical features that can lead to a very accurate reconstruction when the internal code is much shorter than the original dimensionality too. We also discussed how to add sparsity to an autoencoder, and how to use these models to denoise samples. A slightly different variant of a standard autoencoder is a variational autoencoder, which is a generative model that can improve the ability to learn the data-generating process from which a dataset is supposed to be drawn.

Sanger's and Rubner-Tavan's networks are neural models...