I think most of you will not agree to this, but I managed to optimise the parameters w/o the GD algorithm. Of course it was not easy and efficient as GD gives direction and some magnitude to approach minima of the function.

Is there any other reason apart from this or the use GD depends solely on this reason?

Congratulations on optimizing the parameters without using the Gradient Descent (GD) algorithm!
Gradient Descent is popular due to its efficiency, convergence, automatic updates, scalability, regularization, parallelism, and variants, making it a versatile optimization choice despite alternative methods.

Hello @tbhaxor ,
Could you share what your strategy was? Make sure to check if what you did wasnâ€™t a specific case that works only in a particular situation.

Hello @tbhaxor ,
Gradient Descent automatically adjusts model parameters by using gradient information to move in the direction of decreasing loss, reaching the optimal values over iterations.