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Active Machine Learning with Python

Name: Active Machine Learning with Python
Author: Margaux Masson-Forsythe
ISBN: 9781835464946

by Margaux Masson-Forsythe

March 2024

Intermediate to advanced

176 pages

4h 9m

English

Packt Publishing

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Active Machine Learning with Python
ContributorsAbout the authorAbout the reviewer
Preface
Who this book is forWhat this book coversTo get the most out of this bookDownload the example code filesConventions usedGet in touchShare Your ThoughtsDownload a free PDF copy of this book
Part 1: Fundamentals of Active Machine Learning
Chapter 1: Introducing Active Machine Learning
Understanding active machine learning systemsDefinitionPotential range of applicationsKey components of active machine learning systemsExploring query strategies scenariosMembership query synthesisStream-based selective samplingPool-based samplingComparing active and passive learningSummary
Chapter 2: Designing Query Strategy Frameworks
Technical requirementsExploring uncertainty sampling methodsUnderstanding query-by-committee approachesMaximum disagreementVote entropyAverage KL divergenceLabeling with EMC samplingSampling with EERUnderstanding density-weighted sampling methodsSummary
Chapter 3: Managing the Human in the Loop
Technical requirementsDesigning interactive learning systems and workflowsExploring human-in-the-loop labeling toolsCommon labeling platformsHandling model-label disagreementsProgrammatically identifying mismatchesManual review of conflictsEffectively managing human-in-the-loop systemsEnsuring annotation quality and dataset balanceAssess annotator skillsUse multiple annotatorsBalanced samplingSummary
Part 2: Active Machine Learning in Practice
Chapter 4: Applying Active Learning to Computer Vision
Technical requirementsImplementing active ML for an image classification projectBuilding a CNN for the CIFAR datasetApplying uncertainty sampling to improve classification performanceApplying active ML to an object detection projectPreparing and training our modelAnalyzing the evaluation metricsImplementing an active ML strategyUsing active ML for a segmentation projectSummary
Chapter 5: Leveraging Active Learning for Big Data
Technical requirementsImplementing ML models for video analysisSelecting the most informative frames with LightlyUsing Lightly to select the best frames to label for object detectionSSL with active MLSummary
Part 3: Applying Active Machine Learning to Real-World Projects

Chapter 6: Evaluating and Enhancing Efficiency
Technical requirementsCreating efficient active ML pipelinesMonitoring active ML pipelinesDetermining when to stop active ML runsEnhancing production model monitoring with active MLChallenges in monitoring production modelsActive ML to monitor models in productionEarly detection for data drift and model decaySummary
Chapter 7: Utilizing Tools and Packages for Active ML
Technical requirementsMastering Python packages for enhanced active MLscikit-learnmodALGetting familiar with the active ML toolsSummary
Index
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Content preview from Active Machine Learning with Python

2 Designing Query Strategy Frameworks

Query strategies act as the engine that drives active ML and determines which data points get selected for labeling. In this chapter, we aim to provide a comprehensive and detailed explanation of the most widely used and highly effective query strategy frameworks that are employed in active ML. These frameworks play a crucial role in the field of active ML, aiding in selecting informative and representative data points for labeling. The strategies that we will delve into include uncertainty sampling, query-by-committee, expected model change (EMC), expected error reduction (EER), and density-weighted methods. By thoroughly understanding these frameworks and the underlying principles, you can make informed ...

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Active Machine Learning with Python

by Margaux Masson-Forsythe

2

Designing Query Strategy Frameworks

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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