Mastering Self-Supervised Algorithms for Learning without Labels

Mastering Self-Supervised Algorithms for Learning without Labels


31 Lessons


Certificate of Completion

AI Explanations
AI Explanations
Mastering Self-Supervised Algorithms for Learning without Labels
41 Playgrounds
110 Illustrations

Takeaway Skills

An understanding of self-supervised learning and its advantage over unsupervised learning

Working knowledge of designing your self-supervised learning tasks/objectives

Hands-on experience implementing and modifying existing self-supervised learning objectives to learn from unlabelled data

Ability to transfer and evaluate your self-supervised network representations on a downstream task

Familiarity with core components of self-supervised learning, including pretext tasks, similarity maximization, redundancy reduction, and masked image modeling

Course Overview

This course covers self-supervised algorithms, which are useful for large pools of unlabelled data or when obtaining a high-quality labeled dataset is difficult. These algorithms leverage the supervisory signals from the structure of the unlabeled data to predict any unobserved or hidden property of the input. You’ll start with the fundamentals of self-supervised learning and then implement your first class of algorithms. You’ll learn to generate pseudo labels and use these labels for training models using...Show More


How You'll Learn

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Progress You Can Show

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