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The Regularization Cookbook

You're reading from   The Regularization Cookbook Explore practical recipes to improve the functionality of your ML models

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Product type Paperback
Published in Jul 2023
Publisher Packt
ISBN-13 9781837634088
Length 424 pages
Edition 1st Edition
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Author (1):
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Vincent Vandenbussche Vincent Vandenbussche
Author Profile Icon Vincent Vandenbussche
Vincent Vandenbussche
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Toc

Table of Contents (14) Chapters Close

Preface 1. Chapter 1: An Overview of Regularization 2. Chapter 2: Machine Learning Refresher FREE CHAPTER 3. Chapter 3: Regularization with Linear Models 4. Chapter 4: Regularization with Tree-Based Models 5. Chapter 5: Regularization with Data 6. Chapter 6: Deep Learning Reminders 7. Chapter 7: Deep Learning Regularization 8. Chapter 8: Regularization with Recurrent Neural Networks 9. Chapter 9: Advanced Regularization in Natural Language Processing 10. Chapter 10: Regularization in Computer Vision 11. Chapter 11: Regularization in Computer Vision – Synthetic Image Generation 12. Index 13. Other Books You May Enjoy

Deep Learning Reminders

Deep learning is the specific domain of machine learning based on neural networks. Deep learning is known to be particularly powerful with unstructured data, such as text, audio, and image, but can be useful for time series and structured data too. In this chapter, we will review the basics of deep learning, from a perceptron to training a neural network. We will provide recipes for training neural networks for three main use cases: regression, binary classification, and multiclass classification.

In this chapter, we’ll cover the following recipes:

  • Training a perceptron
  • Training a neural network for regression
  • Training a neural network for binary classification
  • Training a multiclass classification neural network
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