Phase diagram of Stochastic Gradient Descent in high-dimensional two-layer neural networks

The episode discusses 'Phase diagram of Stochastic Gradient Descent in high-dimensional two-layer neural networks,' which maps out how training a simple network behaves under different scaling conditions. Hosts explain that learning success depends critically on the relationship between the learning rate, hidden unit width, and input dimension.

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