is an optimization algorithm that combines the benefits of gradient descent and momentum to accelerate convergence
Question
Solution 1
The algorithm you're referring to is called Stochastic Gradient Descent with Momentum (SGD with Momentum). Here's a step-by-step explanation:
Initialize the weights (parameters) randomly.
Calculate the gradient of the loss function with respect to each parameter at the current position.
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