This course includes
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...
What You'll Learn
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
What You'll Learn
An understanding of self-supervised learning and its advantage over unsupervised learning
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Course Content
Introduction to Self-Supervised Learning
Pretext Tasks
Similarity Maximization and Redundancy Reduction
Masked Image Modeling
Appendix
Course Author
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Anthony Walker
@_webarchitect_
Emma Bostian 🐞
@EmmaBostian
Evan Dunbar
ML Engineer
Carlos Matias La Borde
Software Developer
Souvik Kundu
Front-end Developer
Vinay Krishnaiah
Software Developer
Eric Downs
Musician/Entrepeneur
Kenan Eyvazov
DevOps Engineer
Souvik Kundu
Front-end Developer
Eric Downs
Musician/Entrepeneur
Anthony Walker
@_webarchitect_
Emma Bostian 🐞
@EmmaBostian
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