👉 Back computing, also known as backpropagation, is a fundamental algorithm used in training artificial neural networks. It involves calculating the gradient of the loss function with respect to each weight by applying the chain rule of calculus, effectively propagating errors backward through the network from the output layer to the input layer. This process adjusts the weights in the network to minimize the loss, thereby improving the model's predictive accuracy. Backpropagation is essential for deep learning models as it enables efficient and effective training of complex networks with many layers, allowing them to learn intricate patterns in data.
