Human-In-The-Loop Machine Learning: Active Learning and Annotation for Human-Centered AI (Paperback)
暫譯: 人機協作的機器學習：以人為中心的AI的主動學習與標註

Monarch

Human-In-The-Loop Machine Learning: Active Learning and Annotation for Human-Centered AI (Paperback)
  • 出版商: Manning
  • 出版日期: 2021-10-08
  • 定價: $2,150
  • 售價: 8.8 折 $1,892 (限時優惠至 2025-03-31)
  • 語言: 英文
  • 頁數: 424
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1617296740
  • ISBN-13: 9781617296741
  • 相關分類: 人工智慧、Machine Learning

    • 立即出貨 (庫存 < 3)

商品描述

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

Sayak Paul, PyimageSearch

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.

商品描述(中文翻譯)

引用：
一個優秀的參考資料，從實務和理論的角度學習主動學習。

—— Sayak Paul, PyimageSearch

大多數當今部署的機器學習系統都依賴人類反饋進行學習。然而，大多數機器學習課程幾乎專注於算法，而忽略了系統的人機互動部分。這可能會為在現實世界中工作的資料科學家留下很大的知識空白，因為資料科學家在數據管理上花費的時間比在構建算法上要多。Human-in-the-Loop Machine Learning 是一本實用指南，旨在優化整個機器學習過程，包括標註、主動學習、轉移學習的技術，以及利用機器學習來優化每一步驟的過程。

關於技術

機器學習應用在有了人類反饋後表現更佳。保持合適的人員參與可以提高模型的準確性，減少數據錯誤，降低成本，並幫助您更快地交付模型。

關於本書

Human-in-the-Loop Machine Learning 提出了人類與機器有效合作的方法。您將找到選擇樣本數據以獲取人類反饋的最佳實踐、對人類標註的質量控制以及設計標註介面的建議。您將學會為標註、物體檢測、語義分割、序列標註等創建訓練數據。本書從基礎開始，逐步進入轉移學習和自我監督等高級技術在標註工作流程中的應用。

作者簡介

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.

作者簡介(中文翻譯)

羅伯特 (Munro) 莫納克是一位數據科學家和工程師，曾為蘋果、亞馬遜、谷歌和IBM等公司構建機器學習數據。他擁有斯坦福大學的博士學位。

羅伯特的博士研究專注於醫療保健和災難應對中的人機協作機器學習，除了是一名機器學習專業人士外，他還是一名災難應對專業人士。本書中的一個實例是對羅伯特過去協助應對的真實災難中的災難相關信息進行分類。

目錄大綱

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

目錄大綱(中文翻譯)

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

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