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Machine learning design patterns

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76.3 5.8折 132 全新

库存18件

湖北武汉

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作者(美)瓦利阿帕·拉克什曼南,(美)萨拉·罗宾逊,(美)迈克尔·穆恩

出版社东南大学出版社

ISBN9787564195540

出版时间2021-07

装帧平装

开本16开

定价132元

货号9787564195540

上书时间2024-09-02

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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

Solution

Trade-Offs and Alternatives

Summary

4.Model Training Patterns

Typical Training Loop

Stochastic Gradient Descent

Keras Training Loop

Training Design Patterns

Design Pattern 11:Useful Overfitting

Problem

Solution

Why It Works

Trade-Offs and Alternatives

Design Pattern 12:Checkpoints

Problem

Solution

Why It Works

Trade-Offs and Alternatives

Design Pattern 13:Transfer Learning

Problem

Solution

Why It Works

Trade-Offs and Alternatives

Design Pattern 14:Distribution Strategy

Problem

Solution

Why It Works

Trade-Offs and Alternatives

Design Pattern 15:Hyperparameter Tuning

Problem

Solution

Why It Works

Trade-Offs and Alternatives

Summary

5.Design Patterns for Resilient Serving

Design Pattern 16:Stateless Serving Function

Problem

Solution

Why It Works

Trade-Offs and Alternatives

Design Pattern 17:Batch Serving

Problem

Solution

Why It Works

Trade-Offs and Alternatives

Design Pattern 18:Continued Model Evaluation

Problem

Solution

Why It Works

Trade-Offs and Alternatives

Design Pattern 19:Two-Phase Predictions

Problem

Solution

Trade-Offs and Alternatives

Design Pattern 20:Keyed Predictions

Problem

Solution

Trade-Offs and Alternatives

Summary

6.Reproducibility Design Patterns

Design Pattern 21:Transform

Problem

Solution

Trade-Offs and Alternatives

Design Pattern 22:Repeatable Splitting

Problem

Solution

Trade-Offs and Alternatives

Design Pattern 23:Bridged Schema

Problem

Solution

Trade-Offs and Alternatives

Design Pattern 24:Windowed Inference

Problem

Solution

Trade-Offs and Alternatives

Design Pattern 25:Workflow Pipeline

Problem

Solution

Why It Works

Trade-Offs and Alternatives

Design Pattern 26:Feature Store

Problem

Solution

Why It Works

Trade-Offs and Alternatives

Design Pattern 27:Model Versioning

Problem

Solution

Trade-Offs and Alternatives

Summary

7.Responsible AI

Design Pattern 28:Heuristic Benchmark

Problem

Solution

Trade-Offs and Alternatives

Design Pattern 29:Explainable Predictions

Problem

Solution

Trade-Offs and Alternatives

Design Pattern 30:Fairness Lens

Problem

Solution

Trade-Offs and Alternatives

Summary

……

8.Connected Patterns

Index

内容摘要
本书中的设计模式捕捉了机器学习中反复出现的问题的很好实践和解决方案。作者是谷歌的三名工程师，他们整理了已证实的方法，帮助数据科学家解决整个ML过程中的常见问题。这些设计模式将数百位专家的经验编纂成直接、平易近人的建议。在这本书中，你会找到关于数据和问题表示、操作化、可重复性、可再现性、灵活性、可解释性和公平性的30种模式的详细解释。每个模式包括对问题的描述、各种可能的解决方案，以及针对您的情况选择很好技术的建议。

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Machine learning design patterns

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