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Understanding Self-Supervised Learning
This chapter explores the intricacies of self-supervised learning, focusing on its reliance on worldly signals without heavy human involvement. It contrasts this approach with supervised learning, emphasizing how the vast data available can enhance predictive abilities. Key discussions include the challenges of modeling uncertainties in language and vision, as well as the need for efficient representations of complex relationships within data.