Active Machine Learning with Python: Refine and elevate data quality over quantity with active learning

Cover....1 Title Page....2 Copyright and Credits....3 Contributors....5 Table of Contents....8 Preface....12 Part 1: Fundamentals of Active Machine Learning....18 Chapter 1: Introducing Active Machine Learning....20 Understanding active machine learning systems....20 Definition....21 Potential range of applications....21 Key components of active machine learning systems....22 Exploring query strategies scenarios....24 Membership query synthesis....24 Stream-based selective sampling....25 Pool-based sampling....28 Comparing active and passive learning....30 Summary....31 Chapter 2: Designing Query Strategy Frameworks....34 Technical requirements....35 Exploring uncertainty sampling methods....35 Understanding query-by-committee approaches....42 Maximum disagreement....43 Vote entropy....45 Average KL divergence....47 Labeling with EMC sampling....51 Sampling with EER....54 Understanding density-weighted sampling methods....55 Summary....61 Chapter 3: Managing the Human in the Loop....62 Technical requirements....62 Designing interactive learning systems and workflows....63 Exploring human-in-the-loop labeling tools....67 Common labeling platforms....68 Handling model-label disagreements....69 Programmatically identifying mismatches....69 Manual review of conflicts....71 Effectively managing human-in-the-loop systems....72 Ensuring annotation quality and dataset balance....74 Assess annotator skills....74 Use multiple annotators....75 Balanced sampling....76 Summary....78 Part 2: Active Machine Learning in Practice....80 Chapter 4: Applying Active Learning to Computer Vision....82 Technical requirements....82 Implementing active ML for an image classification project....83 Building a CNN for the CIFAR dataset....84 Applying uncertainty sampling to improve classification performance....90 Applying active ML to an object detection project....93 Preparing and training our model....94 Analyzing the evaluation metrics....96 Implementing an active ML strategy....97 Using active ML for a segmentation project....101 Summary....105 Chapter 5: Leveraging Active Learning for Big Data....106 Technical requirements....106 Implementing ML models for video analysis....107 Selecting the most informative frames with Lightly....109 Using Lightly to select the best frames to label for object detection....110 SSL with active ML....132 Summary....135 Part 3: Applying Active Machine Learning to Real-World Projects....136 Chapter 6: Evaluating and Enhancing Efficiency....138 Technical requirements....138 Creating efficient active ML pipelines....139 Monitoring active ML pipelines....141 Determining when to stop active ML runs....144 Enhancing production model monitoring with active ML....145 Challenges in monitoring production models....145 Active ML to monitor models in production....147 Early detection for data drift and model decay....149 Summary....150 Chapter 7: Utilizing Tools and Packages for Active ML....152 Technical requirements....152 Mastering Python packages for enhanced active ML....153 scikit-learn....153 modAL....156 Getting familiar with the active ML tools....162 Summary....164 Index....166 Other Books You May Enjoy....173