An optimizer helps a neural network learn by moving its weights step-by-step. What key information does an optimizer like Adam use from the loss function to know which way to move?

An optimizer like Adam primarily uses the gradient of the loss function with respect to the network's weights to determine which way to move. The loss function is a mathematical measure of how well the neural network is performing, quantifying the discrepancy between its predictions and the actual target values. The gradient is a vector that points in the direction of the steepest increase of this loss function. To minimize the loss, the optimizer moves the weights (the adjustable parameters within the network) in the exact opposite direction of this gradient, essentially taking a step downhill on the loss landscape. This tells the optimizer the most direct path to reduce the error. Beyond this fundamental gradient information, Adam refines its understanding of the optimal movement by incorporating historical grad....