Active Machine Learning with Python - Refine and Elevate Data Quality over Quantity with Active Learning
Building accurate machine learning models requires quality data-lots of it. However, for most teams, assembling massive datasets is time-consuming, expensive, or downright impossible. Led by author, a seasoned ML engineer and advocate for surgical data science and climate AI advancements, this hands...
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| Format: | eBook |
| Language: | English |
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Birmingham
Packt Publishing
2024
Packt Publishing, Limited |
| Edition: | 1 |
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| ISBN: | 9781835464946, 1835464947 |
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| Abstract | Building accurate machine learning models requires quality data-lots of it. However, for most teams, assembling massive datasets is time-consuming, expensive, or downright impossible. Led by author, a seasoned ML engineer and advocate for surgical data science and climate AI advancements, this hands-on guide to active machine learning demonstrates how to train robust models with just a fraction of the data using Python's powerful active learning tools. You'll master the fundamental techniques of active learning, such as membership query synthesis, stream-based sampling, and pool-based sampling and gain insights for designing and implementing active learning algorithms with query strategy and Human-in-the-Loop frameworks. Exploring various active machine learning techniques, you'll learn how to enhance the performance of computer vision models like image classification, object detection, and semantic segmentation and delve into a machine AL method for selecting the most informative frames for labeling large videos, addressing duplicated data. You'll also assess the effectiveness and efficiency of active machine learning systems through performance evaluation. By the end of the book, you'll be able to enhance your active learning projects by leveraging Python libraries, frameworks, and commonly used tools. |
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| AbstractList | Building accurate machine learning models requires quality data-lots of it. However, for most teams, assembling massive datasets is time-consuming, expensive, or downright impossible. Led by author, a seasoned ML engineer and advocate for surgical data science and climate AI advancements, this hands-on guide to active machine learning demonstrates how to train robust models with just a fraction of the data using Python's powerful active learning tools. You'll master the fundamental techniques of active learning, such as membership query synthesis, stream-based sampling, and pool-based sampling and gain insights for designing and implementing active learning algorithms with query strategy and Human-in-the-Loop frameworks. Exploring various active machine learning techniques, you'll learn how to enhance the performance of computer vision models like image classification, object detection, and semantic segmentation and delve into a machine AL method for selecting the most informative frames for labeling large videos, addressing duplicated data. You'll also assess the effectiveness and efficiency of active machine learning systems through performance evaluation. By the end of the book, you'll be able to enhance your active learning projects by leveraging Python libraries, frameworks, and commonly used tools. |
| Author | Masson-Forsythe Margaux |
| Author_xml | – fullname: Masson-Forsythe Margaux |
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| Copyright | 2024 |
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| DEWEY | 943.005 |
| DOI | 10.0000/9781835462683 |
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| Publisher | Packt Publishing Packt Publishing, Limited |
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| Snippet | Building accurate machine learning models requires quality data-lots of it. However, for most teams, assembling massive datasets is time-consuming, expensive,... |
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| SubjectTerms | Data mining Machine learning Programming Languages Python (Computer program language) Software Engineering |
| TableOfContents | Title Page
Preface
Table of Contents
Part I. Fundamentals of Active Machine Learning
1. Introducing Active Machine Learning
2. Designing Query Strategy Frameworks
3. Managing the Human in the Loop
Part II. Active Machine Learning in Practice
4. Applying Active Learning to Computer Vision
5. Leveraging Active Learning for Big Data
Part III. Applying Active Machine Learning to Real-World Projects
6. Evaluating and Enhancing Efficiency
7. Utilizing Tools and Packages for Active ML
Index 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 -- Other Books You May Enjoy Cover -- Title Page -- Copyright and Credits -- Contributors -- Table of Contents -- Preface -- 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 |
| Title | Active Machine Learning with Python - Refine and Elevate Data Quality over Quantity with Active Learning |
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