Margaux Masson-Forsythe

Active Machine Learning with Python (eBook, ePUB)

Refine and elevate data quality over quantity with active learning

• Format: ePub

Produktbeschreibung

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 Margaux Masson-Forsythe, 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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Produktdetails

• Verlag: Packt Publishing
• Seitenzahl: 176
• Erscheinungstermin: 29. März 2024
• Englisch
• ISBN-13: 9781835462683
• Artikelnr.: 70294181

Autorenporträt

Margaux Masson-Forsythe is a skilled machine learning engineer and advocate for advancements in surgical data science and climate AI. As the Director of Machine Learning at Surgical Data Science Collective, she builds computer vision models to detect surgical tools in videos and track procedural motions. Masson-Forsythe manages a multidisciplinary team and oversees model implementation, data pipelines, infrastructure, and product delivery. With a background in computer science and expertise in machine learning, computer vision, and geospatial analytics, she has worked on projects related to reforestation, deforestation monitoring, and crop yield prediction.