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

Categorizing Images of Clothing with Convolutional Neural Networks

The previous chapter wrapped up our coverage of the best practices for general and traditional machine learning. Starting from this chapter, we will dive into the more advanced topics of deep learning and reinforcement learning.

When we deal with image classification, we usually flatten the images and get vectors of pixels and feed them to a neural network (or another model). Although this might do the job, we lose critical spatial information. In this chapter, we will use Convolutional Neural Networks (CNNs) to extract rich and distinguishable representations from images. You will see how CNN representations make a "9" a "9", a "4" a "4", a cat a cat, or a dog a dog.

We will start with exploring individual building blocks in the CNN architecture. Then, we will develop a CNN classifier in TensorFlow to categorize clothing images and demystify the convolutional mechanism...