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Stochastic gradient descent is also known as on-line gradient descent.
The optimization of descent is not the same as that of the optimization of climb.
Which of the following statements is true about stochastic gradient descent?
Stochastic gradient descent (SGD) methods handle redundancy in the data much more efficiently than batch methods.
Which of the following statements is not true about the stochastic gradient descent?
Which of the following statements is not true about stochastic gradient descent?
In stochastic gradient descent the high variance frequent parameter updates causes the loss function to fluctuate heavily.
Stochastic gradient descent has the possibility of escaping from local minima.
Stochastic gradient descent cannot be used for risk minimisation.
Stochastic gradient descent can be used for convex-smooth learning problems.