The Regularization Cookbook: Explore practical recipes to improve the functionality of your ML models - Softcover

Vandenbussche, Vincent

Softcover

ISBN 10: 1837634084 ISBN 13: 9781837634088

Publisher: Packt Publishing, 2023

Synopsis

Methodologies and recipes to regularize any machine learning and deep learning model using cutting-edge technologies such as stable diffusion, Dall-E and GPT-3

Purchase of the print or Kindle book includes a free PDF eBook

Key Features

• Learn to diagnose the need for regularization in any machine learning model
• Regularize different ML models using a variety of techniques and methods
• Enhance the functionality of your models using state of the art computer vision and NLP techniques

Book Description

Regularization is an infallible way to produce accurate results with unseen data, however, applying regularization is challenging as it is available in multiple forms and applying the appropriate technique to every model is a must. The Regularization Cookbook provides you with the appropriate tools and methods to handle any case, with ready-to-use working codes as well as theoretical explanations.

After an introduction to regularization and methods to diagnose when to use it, you’ll start implementing regularization techniques on linear models, such as linear and logistic regression, and tree-based models, such as random forest and gradient boosting. You’ll then be introduced to specific regularization methods based on data, high cardinality features, and imbalanced datasets. In the last five chapters, you’ll discover regularization for deep learning models. After reviewing general methods that apply to any type of neural network, you’ll dive into more NLP-specific methods for RNNs and transformers, as well as using BERT or GPT-3. By the end, you’ll explore regularization for computer vision, covering CNN specifics, along with the use of generative models such as stable diffusion and Dall-E.

By the end of this book, you’ll be armed with different regularization techniques to apply to your ML and DL models.

What you will learn

• Diagnose overfitting and the need for regularization
• Regularize common linear models such as logistic regression
• Understand regularizing tree-based models such as XGBoos
• Uncover the secrets of structured data to regularize ML models
• Explore general techniques to regularize deep learning models
• Discover specific regularization techniques for NLP problems using transformers
• Understand the regularization in computer vision models and CNN architectures
• Apply cutting-edge computer vision regularization with generative models

Who this book is for

This book is for data scientists, machine learning engineers, and machine learning enthusiasts, looking to get hands-on knowledge to improve the performances of their models. Basic knowledge of Python is a prerequisite.

Table of Contents

1. Product Information Document
2. An Overview of Regularization
3. Machine Learning Refresher
4. Regularization with Linear Models
5. Regularization with Tree-Based Models
6. Regularization with Data
7. Deep Learning Reminders
8. Deep Learning Regularization
9. Regularization with Recurrent Neural Networks
10. Advanced Regularization in Natural Language Processing
11. Regularization in Computer Vision
12. Regularization in Computer Vision – Synthetic Image Generation

About this title

About the Author

Since gaining his Ph.D. in nuclear physics, Vincent Vandenbussche has worked for a decade in in diverse companes, deploying ML solutions at scale. He has worked at numerous companies, such as Renault, L'Oréal, General Electric, Jellysmack, Chanel, and CERN.
 
He also has a passion for teaching: he co-founded a data science bootcamp, was an ML lecturer at Mines Paris engineering school and EDHEC business school and trained numerous professionals in companies like ArcelorMittal and Orange.

Edition details

• Publisher: Packt Publishing
• Publication date: 2023
• Language: English
• ISBN 10: 1837634084
• ISBN 13: 9781837634088
• Binding: Paperback
• Edition number: 1
• Number of pages: 424