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The Unsupervised Learning Workshop
book

The Unsupervised Learning Workshop

by Aaron Jones, Christopher Kruger, Benjamin Johnston, Richard Brooker, John Wesley Doyle, Priyanjit Ghosh, Sani Kamal, Ashish Pratik Patil, Philip Solomon, Geetank Raipuria
July 2020
Intermediate to advanced content levelIntermediate to advanced
550 pages
9h 58m
English
Packt Publishing
Content preview from The Unsupervised Learning Workshop

6. t-Distributed Stochastic Neighbor Embedding

Overview

In this chapter, we will discuss Stochastic Neighbor Embedding (SNE) and t-Distributed Stochastic Neighbor Embedding (t-SNE) as a means of visualizing high-dimensional datasets. We will implement t-SNE models in scikit-learn and explain the limitations of t-SNE. Being able to extract high-dimensional information into lower dimensions will prove helpful for visualization and exploratory analysis, as well as being helpful in conjunction with the clustering algorithms we explored in prior chapters. By the end of this chapter, we will be able to find clusters in high-dimensional data, such as user-level information or images in a low-dimensional space.

Introduction

So far, we have described ...

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Publisher Resources

ISBN: 9781800200708Supplemental Content