Keyword Analysis & Research: newton raphson method vs gradient descent

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Frequently Asked Questions

What is the difference between gradient descent and Newton's method?

If you simply compare Gradient Descent and Newton's method, the purpose of the two methods are different. Gradient Descent is used to find (approximate) local maxima or minima (x to make min f (x) or max f (x)). While Newton's method is to find (approximate) the root of a function, i.e. x to make f (x) = 0

What is gradient descent and gradient ascent?

The gradient descent is a first order optimization algorithm. To find a local minimum of a function using gradient descent, one takes steps proportional to the negative of the gradient (or of the approximate gradient) of the function at the current point. The procedure is then known as gradient ascent.

What is the gradient descent method of walking?

The gradient descent way: You look around your feet and no farther than a few meters from your feet. You find the direction that slopes down the most and then walk a few meters in that direction. Then you stop and repeat the process until you can repeat no more. This will eventually lead you to the valley!

What is stochastic gradient descent?

Stochastic gradient descent is a stochastic approximation of the gradient descent optimization method for minimizing an objective function that is written as a sum of differentiable functions. Both statistical estimation and machine learning consider the problem of minimizing an objective function that has the form of a sum:

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