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- Herausgeber: Packt Publishing
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
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.
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Seitenzahl: 412
Veröffentlichungsjahr: 2023
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The Regularization Cookbook
Explore practical recipes to improve the functionality of your ML models
Vincent Vandenbussche
BIRMINGHAM—MUMBAI
The Regularization Cookbook
Copyright © 2023 Packt Publishing
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Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book.
Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
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First published: July 2023
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Published by Packt Publishing Ltd.
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ISBN 978-1-83763-408-8
www.packtpub.com
Writing a book mirrors the kaleidoscope of life, encompassing its ups and downs, joys and sorrows, the quest for purpose, fulfillment, and the haunting whispers of inner emptiness. In this swirling storm of intense emotions, Amandine’s unwavering support served as my guiding light, keeping me on course, and it is with deep appreciation that I dedicate this book to her.
– Vincent Vandenbussche
Foreword
In the ever-evolving landscape of machine learning, it can often feel like you’re trying to navigate a complex labyrinth of algorithms, models, and methods. Among the multitude of concepts that make up this domain, regularization plays an essential role – it keeps the balance between bias and variance, helping models to generalize well rather than just memorizing the training data.
As the art and science of machine learning continue to mature, it becomes imperative that we make these technical concepts accessible, intuitive, and practical to practitioners. In this realm, Vincent, with his depth of experience in machine learning, has embarked on a journey to unravel the complexity of regularization in machine learning models. In this cookbook, he offers a structured approach, grounded in practicality, that meticulously covers everything from the basics to advanced regularization techniques.
Beginning with an introduction to the topic, the book lays the groundwork by offering a refresher on machine learning best practices. It then moves on to tackle regularization techniques for both linear and tree-based models, elegantly bridging the gap between theory and practice.
But Vincent’s cookbook does not stop there. It ventures into regularization with data, covering feature aggregation and the handling of imbalanced datasets, followed by insights into regularization for deep learning and recurrent neural networks.
Recognizing the significance of application-specific regularization techniques, Vincent dedicates several chapters to regularization in natural language processing and computer vision. These chapters explore regularization methods specific to these domains, including the use of embeddings, data augmentation, and synthetic image generation.
The final chapter, on synthetic image generation for regularization, is an intriguing intersection of creativity and technology, offering a testament to Vincent’s expertise in cutting-edge techniques.
Throughout this cookbook, Vincent’s deft blend of theory, practice, and cutting-edge knowledge is evident. This book serves not just as a guide, but as a toolbox, filled with practical recipes that you can directly implement and adapt as per your needs.
Whether you are a beginner finding your feet in the world of machine learning or an experienced practitioner looking to deepen your understanding of regularization, this cookbook offers a comprehensive and hands-on approach. It speaks volumes to Vincent’s expertise and his ability to make complex concepts accessible and practical.
This cookbook promises to be an indispensable resource for every machine learning enthusiast’s journey. It is a culmination of Vincent’s vast experience, ingenuity, and desire to share his knowledge with the world. As you embark on this journey with him, it is my hope that this book will guide, inspire, and empower you to harness the true power of regularization in machine learning.
Happy reading and cooking in the world of machine learning!
–Akin Osman Kazakci
Head of Data Innovation Lab at MINES ParisTech and Entrepreneur-in-Residence at Caltech
Contributors
About the author
Since gaining a Ph.D. in physics, Vincent Vandenbussche has worked for a decade in diverse companies, 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 boot camp, was an ML lecturer at MINES ParisTech engineering school and EDHEC Business School, and trained numerous professionals at companies such as ArcelorMittal and Orange.
About the reviewer
Rajat Agrawal is an accomplished data scientist with a keen focus on deep learning and a profound interest in generative AI and language models. With four years of experience in research work and two years in the field, he has established himself as a valuable asset in the realm of data science. Rajat’s expertise in statistical modeling and predictive analytics, coupled with his groundbreaking publication on Lane Detection and Collision Prevention System for Automated Vehicles in Springer, demonstrates his innovative approach to computer vision algorithms. He is dedicated to a noble mission of democratizing AI, ensuring its accessibility to every individual.
My parents’ unwavering support has been instrumental in shaping my career.
