You're reading from Python Machine Learning By Example Unlock machine learning best practices with real-world use cases

Product type Paperback

Published in Jul 2024

Publisher Packt

ISBN-13 9781835085622

Length 518 pages

Edition 4th Edition

Languages

Python

Tools

PyTorch

Concepts

Computer Vision

Author (1):

Yuxi (Hayden) Liu

View More author details

Table of Contents (18) Chapters

Preface

1. Getting Started with Machine Learning and Python

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

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

4. Predicting Online Ad Click-Through with Logistic Regression

5. Predicting Stock Prices with Regression Algorithms

6. Predicting Stock Prices with Artificial Neural Networks

7. Mining the 20 Newsgroups Dataset with Text Analysis Techniques

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

9. Recognizing Faces with Support Vector Machine

10. Machine Learning Best Practices

11. Categorizing Images of Clothing with Convolutional Neural Networks

12. Making Predictions with Sequences Using Recurrent Neural Networks

13. Advancing Language Understanding and Generation with the Transformer Models

14. Building an Image Search Engine Using CLIP: a Multimodal Approach

15. Making Decisions in Complex Environments with Reinforcement Learning

16. Other Books You May Enjoy

17. Index

Categorizing Images of Clothing with Convolutional Neural Networks

The previous chapter wrapped up our coverage of the best practices for general 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, 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 by exploring individual building blocks in the CNN architecture. Then, we will develop a CNN classifier in PyTorch to categorize clothing images and demystify the convolutional mechanism. Finally...