The Regularization Cookbook

Preface

Have you ever wondered why so many machine learning projects fail in production?

In many cases, this is because of a lack of generalization of models, leading to unexpected predictions when facing new, unseen data. This is what regularization is about: making sure a model provides the expected predictions, even when facing new data.

In this book, we will explore many forms of regularization. To accomplish this, we will explore two primary avenues for regularization solutions, depending on the recipes in the chapter:

• When given a machine learning model, how do we regularize it? Regularizing is most suited in applications where the model is already imposed (whether there is a legacy solution to be updated or strong requirements) and the ...