Title: The Regularization Cookbook: Explore practical recipes to improve the functionality of your ML models
Publisher: Packt Publishing
Publication Date: 2023
Language: English
ISBN 10: 1837634084
ISBN 13: 9781837634088
Binding: Soft cover
Condition: New

About this title

Synopsis

Methodologies and recipes to regularize nearly any machine learning and deep learning model using cutting-edge technologies such as Stable Diffusion, GPT-3, and Unity

Key Features

Learn how to diagnose whether regularization is needed for any machine learning model
Regularize different types of ML models using a broad range of techniques and methods
Get the best of your models using state of the art Computer Vision and NLP

Book Description

Deploying machine learning solutions is all about getting robust results on new, unseen data. To achieve such results, one way is regularization. Regularization can take many forms and can be used in many ways, and not all methods apply to all cases. This book aims at providing the right tools and methods to handle any case properly, with ready-to-use working codes as well as theoretical explanations whenever possible.

After an introduction to regularization and methods to diagnose when to use it, we will start implementing regularization techniques on linear models such as linear and logistic regression, and tree-based models such as random forest and gradient boosting.

The book will then introduce specific regularization methods based on data. High cardinality features and imbalanced datasets may require specific regularization methods that will be explored.

In the last four chapters, the book will cover regularization for deep learning models. After reviewing general methods that apply to any type of neural network, the book will dive into more NLP-specific methods for RNNs and transformers, as well as using BERT or GPT-3. We will close with regularization for Computer Vision, covering CNN specifics, as well as the use of generative models such as GANs and Stable Diffusion, and third-party software like Unity.

What you will learn

How to diagnose overfitting properly and when regularization is needed
Regularizing common linear models such as logistic regression
Get a deeper knowledge of regularizing tree-based models such as XGBoost
Leverage structured data to regularize ML models
Learn 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

Whether you are a data scientist, a machine learning engineer, or just a machine learning enthusiast, if you want to get hands-on knowledge of the available methods to improve the performances of your models, this book is for you.

Basic, hands-on knowledge of Python is expected to get the most out of the proposed codes. Also, basic concepts of ML and DL are reminded to smooth the learning curve, no matter their level. This book is also aimed at experienced professionals willing to use state-of-the-art methods for regularization.

Overview of Regularization
Regularization in ML
Regularization with ML models
Regularization with Tree based models
Regularization with structured data
Deep learning reminders
Regularization of a vanilla neural network
Regularization in NLP
Regularization in Computer Vision
Advanced Regularization in Computer Vision: synthetic image generation

About the Author

After a Ph.D. in Physics, Vincent Vandenbussche has worked for a decade in the industry, deploying ML solutions at scale. He has worked in numerous companies, such as Renault, L’Or�al, 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.

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