Gradient descent is an optimization algorithm for finding the local minimum of a function.

Gradient descent is an optimization algorithm for finding the local minimum of a function. Correct Answer True

Gradient descent is an optimization algorithm for finding the local minimum of a function. It is used to find the values of parameters of a function that minimizes a cost function. The slope of this cost function curve tells us how to update our parameters to make the model more accurate.
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Related Questions

Stochastic gradient descent is also known as on-line gradient descent.
Which is a better algorithm than gradient descent for optimization?
The optimization of descent is not the same as that of the optimization of climb.
Simple gradient descent is a better batch optimization method than conjugate gradients and quasi-newton methods.
Stochastic gradient descent falls under Non-convex optimization.
In SVM problems, we cannot directly apply gradient descent but we can apply Subgradient descent.
Which of the following statements are true/false?
1. A minimum rate of wages for the time spent by employees for the production of output is minimum time rate.
2. Minimum piece rate is the rate based on the quantity of the output employees have produced.
3. The guaranteed time rate is a minimum rate of remuneration to apply in the case of employees employed on piece work for the purpose of securing to such employees a minimum rate of wages on a time work basis.
4. A minimum rate of remuneration to apply in the case of employees employed on piece work for the purpose of securing to such employees a minimum rate of wages on a time work basis is overtime rate.
What is the minimum number of parameters of the gradient descent algorithm?
Stochastic gradient descent has the possibility of escaping from local minima.
The cost function contains a squared term and is divided by 2*m where m is the number of training examples. What is in the denominator of gradient descent function?