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Python Machine Learning By Example

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

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Product type Paperback
Published in Jul 2024
Publisher Packt
ISBN-13 9781835085622
Length 518 pages
Edition 4th Edition
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Author (1):
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Yuxi (Hayden) Liu Yuxi (Hayden) Liu
Author Profile Icon Yuxi (Hayden) Liu
Yuxi (Hayden) Liu
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Table of Contents (18) Chapters Close

Preface 1. Getting Started with Machine Learning and Python FREE CHAPTER 2. Building a Movie Recommendation Engine with Naïve Bayes 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

Summary

In this chapter, we worked on classifying clothing images using CNNs. We started with a detailed explanation of the individual components of a CNN model and learned how CNNs are inspired by the way our visual cells work. We then developed a CNN model to categorize fashion-MNIST clothing images from Zalando. We also talked about data augmentation and several popular image augmentation methods. We practiced transfer learning with ResNets, after discussing the evolution of CNN architectures and pretrained models.

In the next chapter, we will focus on another type of deep learning network: Recurrent Neural Networks (RNNs). CNNs and RNNs are the two most powerful deep neural networks that make deep learning so popular nowadays.

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