Backpropagation, short for “backward propagation of errors,” is a fundamental algorithm used in training artificial neural networks.
Neural networks are computational models inspired by the structure and functioning of the human brain, and they excel in tasks such as pattern recognition, classification, and regression.
The process of training a neural network involves adjusting its internal parameters, known as weights and biases, to minimize the difference between the predicted outputs and the actual targets. Backpropagation is the technique employed to iteratively update these parameters by calculating and adjusting the error associated with each prediction.
Here’s how it works: during the forward pass, input data is processed through the network, layer by layer, to generate predictions. The calculated predictions are then compared to the actual target values, and the error (the difference between prediction and reality) is computed.
In the backward pass, the algorithm works in the opposite direction, starting from the output layer and moving backward through the network. It calculates the contribution of each parameter to the error and adjusts the parameters proportionally to reduce the error in subsequent iterations.
Backpropagation relies on the chain rule from calculus to efficiently compute the gradients of the error concerning each parameter in the network. These gradients guide the direction and magnitude of parameter updates, allowing the neural network to learn from the training data and improve its performance over time.
This iterative process of forward and backward passes is repeated through multiple epochs until the network converges to a state where the error is minimized, and the model effectively generalizes to new, unseen data.
Backpropagation is a cornerstone in the training of neural networks, enabling them to learn complex patterns and relationships in data, and contributing to the advancements in various AI applications.
