The most interesting thing you would first come across when starting out with machine learning is the optimization algorithm and to be specific, it is the gradient descent, which is a first-order iterative optimization algorithm used to minimize the cost function.The intuition behind gradient descent is converging to a solution, which could be a local minimum in the neighborhood or in best-case, the global minimum.Everything seems fine and okay until you start questioning yourself about the convergence problem. Having a good understanding of convexity helps you prove the intuition behind the idea of gradient descent. So let us discuss the same.

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