The Regularization Cookbook: Explore practical recipes to improve the functionality of your ML models

Methodologies and recipes to regularize nearly any machine learning and deep learning model using cutting-edge technologies such as Stable Diffusion, Dall-E and GPT-3.
 
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 five 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 Stable Diffusion and Dall-E.
 
 
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.

本書介紹了使用最新技術（如Stable Diffusion、Dall-E和GPT-3）來規範幾乎任何機器學習和深度學習模型的方法和技巧。

主要特點：

- 學習如何診斷是否需要對任何機器學習模型進行規範化
- 使用各種技術和方法對不同類型的機器學習模型進行規範化
- 利用最先進的計算機視覺和自然語言處理技術獲得最佳模型效果

書籍描述：

部署機器學習解決方案的目標是在新的、未知的數據上獲得穩健的結果。為了實現這樣的結果，一種方法是進行規範化。規範化可以有多種形式，並且可以以多種方式使用，並非所有方法都適用於所有情況。本書旨在提供正確的工具和方法，以適當地處理任何情況，並提供現成的工作代碼以及可能的理論解釋。

在介紹規範化和診斷何時使用規範化方法之後，我們將開始對線性模型（如線性回歸和邏輯回歸）以及基於樹的模型（如隨機森林和梯度提升）實施規範化技術。

本書還介紹了基於數據的特定規範化方法。高基數特徵和不平衡數據集可能需要特定的規範化方法，這些方法將被探討。

在最後五章中，本書將介紹深度學習模型的規範化。在回顧適用於任何類型的神經網絡的一般方法之後，本書將深入探討RNN和transformer的NLP特定方法，以及使用BERT或GPT-3。我們將以計算機視覺的規範化結束，涵蓋CNN的特定內容，以及使用Stable Diffusion和Dall-E等生成模型。

你將學到：

- 如何正確診斷過度擬合以及何時需要規範化
- 對常見的線性模型（如邏輯回歸）進行規範化
- 深入了解規範化基於樹的模型（如XGBoost）
- 利用結構化數據對機器學習模型進行規範化
- 學習規範化深度學習模型的通用技術
- 使用Transformer解決NLP問題的特定規範化技術
- 理解計算機視覺模型和CNN架構中的規範化
- 使用生成模型進行尖端計算機視覺規範化

適合對象：

無論你是數據科學家、機器學習工程師還是機器學習愛好者，如果你想獲得關於改善模型性能的可用方法的實踐知識，本書適合你。預期讀者具備基本的Python實踐知識，並且對機器學習和深度學習的基本概念有所了解。本書也適合有經驗的專業人士，他們希望使用最先進的規範化方法。

1. An Overview of Regularization
2. Machine Learning Refresher
3. Regularization with Linear Models
4. Regularization with Tree-based Models
5. Regularization with Data
6. Deep Learning Reminders
7. Deep Learning Regularization
8. Regularization with Recurrent Neural Networks
9. Advanced Regularization in Natural Language Processing
10. Regularization in Computer Vision
11. Regularization in Computer Vision - synthetic image generation

- 正則化概述
- 機器學習複習
- 線性模型的正則化
- 基於樹的模型的正則化
- 數據的正則化
- 深度學習提醒
- 深度學習的正則化
- 循環神經網絡的正則化
- 自然語言處理中的高級正則化
- 電腦視覺中的正則化
- 電腦視覺中的正則化 - 合成圖像生成