| 作 者： | Valliappa Lakshmanan Sara Robinson Michael |
| 出版社： | 东南大学出版社 |
| 丛编项： | |
| 版权说明： | 本书为公共版权或经版权方授权，请支持正版图书 |
| 标 签： | 暂缺 |
| ISBN | 出版时间 | 包装 | 开本 | 页数 | 字数 |
|---|---|---|---|---|---|
| 未知 | 暂无 | 暂无 | 未知 | 0 | 暂无 |
Preface
1．The Need for Machine Learning Design Patterns
What Are Design Patterns?
How to Use This Book
Machine Learning Terminology
Models and Frameworks
Data and Feature Engineering
The Machine Learning Process
Data and Model Tooling
Roles
Common Chauenges in Machine Learning
Data Quality
Reproducibility
Data Drift
Scale
Multiple Objectives
Summary
2．Data Representation Design Patterns
Simple Data Representations
Numerical Inputs
Categorical Inputs
Design Pattern 1： Hashed Feature
Problem
Solution
Why It Works
Trade-Offs and Alternatives
Design Pattern 2： Embeddings
Problem
Solution
Why It Works
Trade-Offs and Alternatives
Design Pattern 3： Feature Cross
Problem
Solution
Why It Works
Trade-Offs and Alternatives
Design Pattern 4： Multimodallnput
Problem
Solution
Trade-Offs and Alternatives
Summary
3．Problem Representation Design Patterns
Design Pattern 5： Reframing
Problem
Solution
Why It Works
Trade-Offs and Alternatives
Design Pattern 6： Multilabel
Problem
Solution
Trade-Offs and Alternatives
Design Pattern 7： Ensembles
Problem
Solution
Why It Works
Trade-Offs and Alternatives
Design Pattern 8： Cascade
Problem
Solution
Trade-Offs and Alternatives
Design Pattern 9： Neutral Class
Problem
Solution
Why It Works
Trade-Offs and Alternatives
Design Pattern 10： Re alanang
Problem
……
4．ModeI Training Patterns．．．
5．Design Patterns for Resilient Serving
6．Reproduability Design Patterns
7．Responsible AI
8．Connected Patterns
Index
