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

Exploring the clothing image dataset

The clothing 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 seeks to make the dataset as popular as the handwritten digits MNIST dataset (http://yann.lecun.com/exdb/mnist/) 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 Keras, which already includes the dataset and its API. We will take the latter approach, as follows...