You're reading from Python Machine Learning by Example Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn

Product type Paperback

Published in Oct 2020

Publisher Packt

ISBN-13 9781800209718

Length 526 pages

Edition 3rd Edition

Languages

Python

Tools

PyTorch

Concepts

Machine Learning

Author (1):

Yuxi (Hayden) Liu

View More author details

Table of Contents (17) Chapters

Preface

1. Getting Started with Machine Learning and Python

2. Building a Movie Recommendation Engine with Naïve Bayes FREE CHAPTER

3. Recognizing Faces with Support Vector Machine

4. Predicting Online Ad Click-Through with Tree-Based Algorithms

5. Predicting Online Ad Click-Through with Logistic Regression

6. Scaling Up Prediction to Terabyte Click Logs

7. Predicting Stock Prices with Regression Algorithms

8. Predicting Stock Prices with Artificial Neural Networks

9. Mining the 20 Newsgroups Dataset with Text Analysis Techniques

10. Discovering Underlying Topics in the Newsgroups Dataset with Clustering and Topic Modeling

11. Machine Learning Best Practices

12. Categorizing Images of Clothing with Convolutional Neural Networks

13. Making Predictions with Sequences Using Recurrent Neural Networks

14. Making Decisions in Complex Environments with Reinforcement Learning

15. Other Books You May Enjoy

16. Index

Visualizing the newsgroups data with t-SNE

We can answer these questions easily by visualizing those representation vectors. If we can see the document vectors from the same topic form a cluster, we did a good job mapping the documents into vectors. But how? They are of 500 dimensions, while we can visualize data of at most three dimensions. We can resort to t-SNE for dimensionality reduction.

What is dimensionality reduction?

Dimensionality reduction is an important machine learning technique that reduces the number of features and, at the same time, retains as much information as possible. It is usually performed by obtaining a set of new principal features.

As mentioned before, it is difficult to visualize data of high dimension. Given a three-dimensional plot, we sometimes don't find it straightforward to observe any findings, not to mention 10, 100, or 1,000 dimensions. Moreover, some of the features in high dimensional data may be correlated...