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#042 - Pedro Domingos - Ethics and Cancel Culture

Machine Learning Street Talk (MLST)

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Symmetry in Neural Learning

This chapter explores symmetry-based learning in neural networks, focusing on how encoding real-world symmetries can improve efficiency and reduce sample complexity. The discussion includes comparisons between neural network architectures and kernel methods, emphasizing the relevance of experimenting with various architectures to enhance understanding. Additionally, it touches on the broader implications of intelligence in artificial intelligence, suggesting innovative directions beyond traditional methods like gradient descent.

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