In this chapter, we started with an introduction to artificial neural networks, how they are structured, and the processes by which they learn to complete a particular task. Starting with a supervised learning example, we built an artificial neural network classifier to identify objects within the CIFAR-10 dataset. We then progressed to the autoencoder architecture of neural networks and learned how we can use these networks to prepare a dataset for use in an unsupervised learning problem. Finally, we completed this investigation with autoencoders, looking at convolutional neural networks and the benefits these additional layers can provide. This chapter prepared us well for the final instalment in dimensionality reduction, as we look at using and visualizing the encoded data with t-distributed nearest neighbors (t-SNE). T-distributed nearest neighbors provides an extremely effective method of visualizing high-dimensional data even after applying reduction techniques such as PCA...

#### Applied Unsupervised Learning with Python

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#### Applied Unsupervised Learning with Python

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#### 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

Free Chapter

Introduction to Clustering

Hierarchical Clustering

Neighborhood Approaches and DBSCAN

Dimension Reduction and PCA

Autoencoders

t-Distributed Stochastic Neighbor Embedding (t-SNE)

Topic Modeling

Market Basket Analysis

Hotspot Analysis

Appendix

Customer Reviews