January 2025
Intermediate to advanced
698 pages
16h 48m
English
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7.4 Self-Supervised Learning and Foundation Models
7.4.1 What is Self-Supervised Learning?
Self-supervised learning (SSL) is an innovative approach in machine learning that bridges the gap between supervised and unsupervised learning. It leverages the inherent structure within unlabeled data to create supervised learning tasks, effectively allowing the model to learn from itself. This method is particularly valuable in scenarios where labeled data is scarce or expensive to obtain.
At its core, SSL works by formulating pretext tasks that don't require manual labeling. These tasks are carefully designed to force the model to learn meaningful representations of the data. For instance, in computer vision, a model might be tasked with predicting the ...
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