Unsupervised Learning with PyTorch
Master Clustering, Dimensionality Reduction, and Self-Supervised Learning for Real-World Applications
Move beyond supervised learning and discover how to extract meaningful insights from unlabeled data. This practical guide teaches you to implement unsupervised learning algorithms using PyTorch, from foundational clustering techniques to advanced self-supervised methods. You'll learn how to preprocess data effectively, build and train models that discover hidden patterns, and evaluate results without ground truth labels. Each concept is grounded in real-world scenarios—customer segmentation, anomaly detection, feature extraction, and more. With hands-on code examples and clear explanations, you'll gain the confidence to tackle problems where labeled data is scarce or expensive. Whether you're exploring new datasets or building production systems, this book equips you with the techniques and PyTorch expertise to unlock the full potential of your data.
Est. Length
253 pages (13 chapters)
Language
English
Publication date
Dec 9, 2025
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