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Does gradient descent find local maxima?
If Gradient Descent gets initialized in such a way that it starts at a local maximum (or a saddle point, or a local minimum) with gradient zero, then it will simply stay stuck there.
Does gradient descent getting stuck in local optima?
The path of stochastic gradient descent wanders over more places, and thus is more likely to “jump out” of a local minimum, and find a global minimum (Note*). However, stochastic gradient descent can still get stuck in local minimum.
What is local optima in gradient descent?
Gradient descent techniques are known to be limited by a characteristic referred to as the `local minima’ problem. During the search for an optimum solution or global minima, these techniques can encounter local minima from which they cannot escape due to the `steepest descent’ nature of the approach.
When does gradient descent start at a local maximum?
If Gradient Descent gets initialized in such a way that it starts at a local maximum (or a saddle point, or a local minimum) with gradient zero, then it will simply stay stuck there.
How is gradient descent used in machine learning?
Gradient descent is an optimization algorithm that’s used when training a machine learning model. It’s based on a convex function and tweaks its parameters iteratively to minimize a given function to its local minimum.
When to use gradient descent for convex problems?
For convex problems, gradient descent can find the global minimum with ease, but as nonconvex problems emerge, gradient descent can struggle to find the global minimum, where the model achieves the best results. Recall that when the slope of the cost function is at or close to zero, the model stops learning.
How is the cost function used in gradient descent?
Training data helps these models learn over time, and the cost function within gradient descent specifically acts as a barometer, gauging its accuracy with each iteration of parameter updates. Until the function is close to or equal to zero, the model will continue to adjust its parameters to yield the smallest possible error.