The video tutorial discusses the concept of local and global minima in the context of error functions and gradient descent. It explains how error functions can have multiple local minima, which can mislead gradient descent algorithms into thinking they have found the point of minimum error. The tutorial highlights the challenge of reaching the global minima, where the error is truly minimized, and discusses the implications of this in training models.

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