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Comment on Every Model Learned by Gradient Descent Is Approximately a Kernel Machineparent

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No, not necessarily. The objective functions used to train neural networks are generally non-convex (the nets themselves being non-convex as well), but are traditionally trained using stochastic gradient descent (and its variants).

This really threw me off when I first started learning about ANNs, coming from a traditional econometrics background. B-but ... the parameters aren’t identified!

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