Active Machine Learning with Python: Refine and elevate data quality over quantity with active learning [1 ed.] 9781835464946

Table of contents :
Active Machine Learning with Python
Contributors
About the author
About the reviewer
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Conventions used
Get in touch
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Part 1: Fundamentals of Active Machine Learning
Chapter 1: Introducing Active Machine Learning
Understanding active machine learning systems
Definition
Potential range of applications
Key components of active machine learning systems
Exploring query strategies scenarios
Membership query synthesis
Stream-based selective sampling
Pool-based sampling
Comparing active and passive learning
Summary
Chapter 2: Designing Query Strategy Frameworks
Technical requirements
Exploring uncertainty sampling methods
Understanding query-by-committee approaches
Maximum disagreement
Vote entropy
Average KL divergence
Labeling with EMC sampling
Sampling with EER
Understanding density-weighted sampling methods
Summary
Chapter 3: Managing the Human in the Loop
Technical requirements
Designing interactive learning systems and workflows
Exploring human-in-the-loop labeling tools
Common labeling platforms
Handling model-label disagreements
Programmatically identifying mismatches
Manual review of conflicts
Effectively managing human-in-the-loop systems
Ensuring annotation quality and dataset balance
Assess annotator skills
Use multiple annotators
Balanced sampling
Summary
Part 2: Active Machine Learning in Practice
Chapter 4: Applying Active Learning to Computer Vision
Technical requirements
Implementing active ML for an image classification project
Building a CNN for the CIFAR dataset
Applying uncertainty sampling to improve classification performance
Applying active ML to an object detection project
Preparing and training our model
Analyzing the evaluation metrics
Implementing an active ML strategy
Using active ML for a segmentation project
Summary
Chapter 5: Leveraging Active Learning for Big Data
Technical requirements
Implementing ML models for video analysis
Selecting the most informative frames with Lightly
Using Lightly to select the best frames to label for object detection
SSL with active ML
Summary
Part 3: Applying Active Machine Learning to Real-World Projects
Chapter 6: Evaluating and Enhancing Efficiency
Technical requirements
Creating efficient active ML pipelines
Monitoring active ML pipelines
Determining when to stop active ML runs
Enhancing production model monitoring with active ML
Challenges in monitoring production models
Active ML to monitor models in production
Early detection for data drift and model decay
Summary
Chapter 7: Utilizing Tools and Packages for Active ML
Technical requirements
Mastering Python packages for enhanced active ML
scikit-learn
modAL
Getting familiar with the active ML tools
Summary
Index
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