Assertion: gradient descent | /r/CapableOf | fall into local minimum
Facets:
value: easily
type: MANNER
Source sentences:
text: We want a data set that is large and minimizes a non-convex function, so we can observe cases where our gradient descent might fall into a local minimum as opposed to a global one.
text: Traditional gradient descent does not guarantee optimality and in fact, it could easily fall into local minimum when there are multiple basins in the objective function, as parameters each time only move slightly based on gradient and no stochasticity is allowed.