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

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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

Exploring the clothing image dataset

The clothing dataset Fashion-MNIST (https://github.com/zalandoresearch/fashion-mnist) is a dataset of images from Zalando (Europe’s biggest online fashion retailer). It consists of 60,000 training samples and 10,000 test samples. Each sample is a 28 * 28 grayscale image, associated with a label from the following 10 classes, each representing articles of clothing:

0: T-shirt/top
1: Trouser
2: Pullover
3: Dress
4: Coat
5: Sandal
6: Shirt
7: Sneaker
8: Bag
9: Ankle boot

Zalando aims to make the dataset as popular as the handwritten digits MNIST dataset for benchmarking algorithms and hence calls it Fashion-MNIST.

You can download the dataset from the direct links in the Get the data section using the GitHub link or simply import it from PyTorch, which already includes the dataset and its data loader API. We will take the latter approach, as follows:

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Table of Contents (18) Chapters

Exploring the clothing image dataset

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

Table of Contents (18) Chapters

Exploring the clothing image dataset

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