Backpropagation
Backpropagation, short for "backward propagation of errors," is a fundamental algorithm used for training artificial neural networks, particularly deep neural networks with multiple hidden layers. It involves two main phases: a forward pass, where input data is passed through the network to generate an output, and a backward pass, where the error between the predicted output and the actual output is calculated and propagated back through the network.
This backward pass efficiently computes the gradient of the loss function (a measure of the error) with respect to each weight in the network by applying the chain rule of calculus. These gradients are then used to update the weights in a direction that minimally reduces the error, typically through an optimization algorithm like stochastic gradient descent.
Backpropagation is crucial for the learning process in neural networks, allowing them to adjust their weights and biases to improve their predictions.
In image recognition tasks, backpropagation is used to train convolutional neural networks (CNNs) to accurately classify images. During training, an image is passed through the CNN (forward pass), and the network's output is compared to the actual label of the image to compute the error. Backpropagation then calculates the gradients of this error with respect to all the weights in the network, and these weights are adjusted to decrease the error (backward pass). This process is repeated with many images, gradually improving the network's ability to recognize images correctly.
Another example is in natural language processing (NLP), where backpropagation is used in recurrent neural networks (RNNs) for tasks like language translation. The RNN processes input sequences (e.g., sentences in the source language), and backpropagation adjusts the network's parameters to minimize the difference between the predicted translation and the actual translation. This iterative adjustment of parameters enables the network to improve its translation accuracy over time.