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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

Product type Paperback

Published in Jul 2023

Publisher Packt

ISBN-13 9781837634088

Length 424 pages

Edition 1st Edition

Languages

Ring

Tools

Astro

Concepts

Machine Learning

Author (1):

Vincent Vandenbussche

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Table of Contents (14) Chapters

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

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13. Other Books You May Enjoy

Index

As this ebook edition doesn't have fixed pagination, the page numbers below are hyperlinked for reference only, based on the printed edition of this book.

A

activation functions

ReLU 158

sigmoid 157

tanh 157

visualization 158

activation functions, PyTorch

reference link 159

Adaptive momentum (Adam) 149

Albumentations 347

URL 358

used, for applying image augmentation 344

Amazon’s case 3

attention mechanisms 276

AutoTokenizer

reference link 234

Average Precision (AP) 313

B

background images

synthetic dataset, generating from 363-365

backpropagation through time 224

BERT embeddings

using, for regularization 276-282

BERT model card

reference link 282

BERT paper

reference link 282

bias 11

bias and variance 11

cases 12

bias-variance trade-off 16

Bidirectional Encoder Representation from Transformers (BERT) 276

bidirectional RNNs 247

binary classification...

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Authors (1)

Vincent Vandenbussche

Vincent Vandenbussche

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.

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