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Artificial Intelligence with Python Cookbook

You're reading from   Artificial Intelligence with Python Cookbook Proven recipes for applying AI algorithms and deep learning techniques using TensorFlow 2.x and PyTorch 1.6

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Product type Paperback
Published in Oct 2020
Publisher Packt
ISBN-13 9781789133967
Length 468 pages
Edition 1st Edition
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Authors (2):
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Ritesh Kumar Ritesh Kumar
Author Profile Icon Ritesh Kumar
Ritesh Kumar
Ben Auffarth Ben Auffarth
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Ben Auffarth
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Table of Contents (13) Chapters Close

Preface 1. Getting Started with Artificial Intelligence in Python 2. Advanced Topics in Supervised Machine Learning FREE CHAPTER 3. Patterns, Outliers, and Recommendations 4. Probabilistic Modeling 5. Heuristic Search Techniques and Logical Inference 6. Deep Reinforcement Learning 7. Advanced Image Applications 8. Working with Moving Images 9. Deep Learning in Audio and Speech 10. Natural Language Processing 11. Artificial Intelligence in Production 12. Other Books You May Enjoy

Recognizing clothing items

A popular example of image classification is the MNIST dataset, which contains digits from 0 to 9 in different styles. Here, we'll use a drop-in replacement, called Fashion-MNIST, consisting of different pieces of clothing.

Fashion-MNIST is a dataset of Zalando's article images consisting of a training set of 60,000 examples and a test set of 10,000 examples: https://github.com/zalandoresearch/fashion-mnist.

Here are a few examples from the dataset:

In this recipe, we'll recognize clothing items with different models – we'll start with generic image features (Difference of Gaussians, or DoG) and a support vector machine; then we'll move on to a feedforward Multilayer Perceptron (MLP); then we'll use a Convolutional Neural Network (ConvNet); and finally, we'll look at transfer learning with MobileNet.

Getting ready

Before we can start, we have to install a library. In this recipe, we'll use scikit-image, a library...

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