👉 Weights computing in neural networks, particularly in the context of deep learning, refers to the process by which the network learns to adjust the values (weights) of its connections based on the data it processes. During training, the network makes predictions and compares them to actual outcomes, calculating an error or loss function that quantifies how far off these predictions are. This error is then used to update the weights through an optimization algorithm, such as gradient descent, which iteratively adjusts the weights in the direction that minimizes the loss. The magnitude and direction of these weight updates are determined by their current values, learning rates, and the gradients (derivatives) of the loss function with respect to each weight. Over time, this process refines the weights, enabling the network to make more accurate predictions and effectively learn complex patterns from data.
