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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.
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'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.
