Human-In-The-Loop Machine Learning: Active Learning and Annotation for Human-Centered AI (Paperback)

An excellent reference for learning about active learning from both practical and theoretical perspectives.

Most machine learning systems that are deployed in the world today learn from human feedback. However, most machine learning courses focus almost exclusively on the algorithms, not the human-computer interaction part of the systems. This can leave a big knowledge gap for data scientists working in real-world machine learning, where data scientists spend more time on data management than on building algorithms. Human-in-the-Loop Machine Learning is a practical guide to optimizing the entire machine learning process, including techniques for annotation, active learning, transfer learning, and using machine learning to optimize every step of the process.

about the technology

Machine learning applications perform better with human feedback. Keeping the right people in the loop improves the accuracy of models, reduces errors in data, lowers costs, and helps you ship models faster.

about the book

Human-in-the-Loop Machine Learning lays out methods for humans and machines to work together effectively. You’ll find best practices on selecting sample data for human feedback, quality control for human annotations, and designing annotation interfaces. You’ll learn to create training data for labeling, object detection, and semantic segmentation, sequence labeling, and more. The book starts with the basics and progresses to advanced techniques like transfer learning and self-supervision within annotation workflows.

這是一本從實踐和理論角度學習主動學習的優秀參考資料。

Sayak Paul, PyimageSearch

大多數今天在世界上部署的機器學習系統都是從人類反饋中學習的。然而，大多數機器學習課程幾乎完全專注於算法，而不是系統中的人機交互部分。這可能會給在實際機器學習中工作的數據科學家留下一個巨大的知識空白，因為數據科學家在實際機器學習中花費的時間更多是在數據管理上，而不是在構建算法上。《人在迴圈中的機器學習》是一本實用指南，旨在優化整個機器學習過程，包括標註技術、主動學習、遷移學習以及使用機器學習優化每個步驟的技術。

關於技術方面，機器學習應用在人類反饋下表現更好。將合適的人員納入迴圈可以提高模型的準確性，減少數據錯誤，降低成本，並幫助您更快地部署模型。

關於這本書， 《人在迴圈中的機器學習》介紹了人與機器有效合作的方法。您將找到選擇人類反饋樣本數據的最佳實踐、人工標註的質量控制以及設計標註界面的方法。您將學習為標註、物體檢測和語義分割、序列標註等創建訓練數據的技巧。本書從基礎知識開始，逐步深入介紹了在標註工作流程中的遷移學習和自我監督等高級技術。

Robert (Munro) Monarch is a data scientist and engineer who has built machine learning data for companies such as Apple, Amazon, Google, and IBM. He holds a PhD from Stanford.

Robert holds a PhD from Stanford focused on Human-in-the-Loop machine learning for healthcare and disaster response, and is a disaster response professional in addition to being a machine learning professional. A worked example throughout this text is classifying disaster-related messages from real disasters that Robert has helped respond to in the past.

Robert (Munro) Monarch 是一位資料科學家和工程師，曾為蘋果、亞馬遜、谷歌和IBM等公司建立機器學習資料。他在斯坦福大學獲得博士學位。

Robert 在斯坦福大學專注於人機協同機器學習在醫療保健和災害應對方面的研究，除了是一位機器學習專業人士，他還是一位災害應對專業人士。本書中的一個實例是根據Robert過去參與應對的真實災害中的與災害相關的訊息進行分類。

Table of Contents

PART 1 - FIRST STEPS
1 Introduction to human-in-the-loop machine learning
2 Getting started with human-in-the-loop machine learning
PART 2 - ACTIVE LEARNING
3 Uncertainty sampling
4 Diversity sampling
5 Advanced active learning
6 Applying active learning to different machine learning tasks
PART 3 - ANNOTATION
7 Working with the people annotating your data
8 Quality control for data annotation
9 Advanced data annotation and augmentation
10 Annotation quality for different machine learning tasks
PART 4 - HUMAN–COMPUTER INTERACTION FOR MACHINE LEARNING
11 Interfaces for data annotation
12 Human-in-the-loop machine learning products

目錄



第一部分 - 初步

1 人在迴圈機器學習介紹

2 開始進行人在迴圈機器學習

第二部分 - 主動學習

3 不確定性抽樣

4 多樣性抽樣

5 進階主動學習

6 將主動學習應用於不同的機器學習任務

第三部分 - 註解

7 與註解您的數據的人合作

8 數據註解的品質控制

9 進階數據註解和擴充

10 不同機器學習任務的註解品質

第四部分 - 人-電腦互動的機器學習

11 數據註解的介面

12 人在迴圈機器學習產品