Answer / Nikhil Gupta

"No, gradient descent methods do not always converge to the same point or even to an optimal solution. The convergence depends on various factors such as the initial values of weights (the parameters being optimized), the learning rate, and the shape and scale of the cost function landscape. Gradient descent algorithms may get stuck in local minima, preventing them from reaching a global minimum. Techniques like momentum, adaptive learning rates, or regularization can help improve convergence properties."n

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