Book Image

Applied Unsupervised Learning with Python

By : Benjamin Johnston, Aaron Jones, Christopher Kruger
Book Image

Applied Unsupervised Learning with Python

By: Benjamin Johnston, Aaron Jones, Christopher Kruger

Overview of this book

Unsupervised learning is a useful and practical solution in situations where labeled data is not available. Applied Unsupervised Learning with Python guides you in learning the best practices for using unsupervised learning techniques in tandem with Python libraries and extracting meaningful information from unstructured data. The book begins by explaining how basic clustering works to find similar data points in a set. Once you are well-versed with the k-means algorithm and how it operates, you’ll learn what dimensionality reduction is and where to apply it. As you progress, you’ll learn various neural network techniques and how they can improve your model. While studying the applications of unsupervised learning, you will also understand how to mine topics that are trending on Twitter and Facebook and build a news recommendation engine for users. Finally, you will be able to put your knowledge to work through interesting activities such as performing a Market Basket Analysis and identifying relationships between different products. By the end of this book, you will have the skills you need to confidently build your own models using Python.
Table of Contents (12 chapters)
Applied Unsupervised Learning with Python
Preface

Fundamentals of Artificial Neural Networks


Given that autoencoders are based on artificial neural networks, an understanding of how neural networks is also critical for understanding autoencoders. This section of the chapter will briefly review the fundamentals of artificial neural networks. It is important to note that there are many aspects of neural nets that are outside of the scope of this book. The topic of neural networks could easily, and has, filled many books on its own, and this section is not to be considered an exhaustive discussion of the topic.

As described earlier, artificial neural networks are primarily used in supervised learning problems, where we have a set of input information, say a series of images, and we are training an algorithm to map the information to a desired output, such as a class or category. Consider the CIFAR-10 dataset () as an example, which contains images of 10 different categories (airplane, automobile, bird, cat, deer, dog, frog, horse, ship, and...