General Principles in ML
Chris and Tim discuss the power of borrowing strengths from different domains in machine learning models, emphasizing the importance of general inductive biases like symmetries. They delve into the idea of using foundational models to bootstrap various tasks, highlighting the potential for models to extract patterns across diverse domains.In this clip
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