What is the problem with gradient descent algorithm?

What is the problem with gradient descent algorithm?

If the execution is not done properly while using gradient descent, it may lead to problems like vanishing gradient or exploding gradient problems. These problems occur when the gradient is too small or too large. And because of this problem the algorithms do not converge.

How is gradient descent algorithm used for prediction?

Gradient descent is an iterative optimization algorithm for finding the local minimum of a function. To find the local minimum of a function using gradient descent, we must take steps proportional to the negative of the gradient (move away from the gradient) of the function at the current point.

What are the conditions in which gradient descent is applied?

In case of Batch Gradient Descent, the algorithm follows a straight path towards the minimum. If the cost function is convex, then it converges to a global minimum and if the cost function is not convex, then it converges to a local minimum.

How to calculate gradient in gradient descent?

How to understand Gradient Descent algorithm Initialize the weights (a & b) with random values and calculate Error (SSE) Calculate the gradient i.e. change in SSE when the weights (a & b) are changed by a very small value from their original randomly initialized value. Adjust the weights with the gradients to reach the optimal values where SSE is minimized

Why do we use gradient descent in linear regression?

The main reason why gradient descent is used for linear regression is the computational complexity: it’s computationally cheaper (faster) to find the solution using the gradient descent in some cases.

What are the weaknesses of gradient descent?

Weaknesses of Gradient Descent: The learning rate can affect which minimum you reach and how quickly you reach it. If learning rate is too high (misses the minima) or too low (time consuming) Can…

What is steepest descent algorithm?

Steepest Descent. The steepest descent algorithm is an old mathematical tool for numerically finding the minimum value of a function, based on the gradient of that function.