High Dimensional Learning
Tim explains the challenges of high-dimensional learning due to the curse of dimensionality, emphasizing the need for new function spaces based on geometric principles to reduce statistical error and overfitting. By leveraging geometric priors, we can navigate the complex high-dimensional input space more effectively, leading to more reliable machine learning models.In this clip
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