9781837639724_

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

...(*_*)9781837639724_

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, youll start implementing regularization techniques on linear models, such as linear and logistic regression, and tree-based models, such as random forest and gradient boosting. Youll then be introduced to specific regularization methods based on data, high cardinality features, and imbalanced datasets. In the last five chapters, youll discover regularization for deep learning models. After reviewing general methods that apply to any type of neural network, youll dive into more NLP-specific methods for RNNs and transformers, as well as using BERT or GPT-3. By the end, youll 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, youll 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.

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Key Features

Book Description

What you will learn

Who This Book Is For

Table of Contents

Key Features

Book Description

What you will learn

Who this book is for

The Regularization Cookbook

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