Error Retropropagación, often simply called backpropagation, is a fundamental algorithm used in training artificial redes neuronales. It works by calculating the gradient of the función de pérdida with respect to each weight in the network, allowing the model to update its weights to minimize prediction errors.
El proceso comienza con una paso hacia adelante, where input data is fed through the network, and predictions are made. The loss function, which quantifies the difference between the predicted outputs and the actual target values, is then computed. After this, the backpropagation algorithm computes the gradients of the loss function with respect to each weight by applying the chain rule of calculus. This step essentially involves propagating the error from the output layer back through the network layers.
Una vez que se calculan los gradientes, un algoritmo de optimización, such as Stochastic Gradient Descent (SGD), is used to adjust the weights in the opposite direction of the gradient. This adjustment is determined by a learning rate, which controls how much the weights are updated during each iteration. The backpropagation process is repeated over many epochs, iteratively refining the weights to improve the neural network’s performance on the training data.
Backpropagation is essential for enabling deep learning models to learn complex patterns from data, and it is widely used across various applications in machine learning, including image recognition, procesamiento de lenguaje natural, and more.