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Backpropagation is a fundamental algorithm used in training artificial neural networks by efficiently computing gradients needed for optimization. It enables the adjustment of network weights through the chain rule of calculus, facilitating learning in multi-layer networks.
Stochastic gradient descent is an optimization algorithm used in machine learning and statistical modeling to minimize loss functions by iteratively updating parameters using random subsets of data.
The learning rate is a hyperparameter in machine learning algorithms that controls the step size at each iteration while moving toward a minimum of a loss function. It plays a critical role in model training by influencing the speed and quality of convergence.