Backpropagation

Backpropagation (backward propagation of errors) is a machine learning technique used to train neural networks by calculating how errors in the output propagate backward through earlier layers. This information is then used to adjust the network and improve the model’s predictions. The process has four main steps.

• Forward pass: Input data moves through the network to produce an output.
• Error calculation: The output is compared to the correct answer using a loss function, which measures how far the prediction is from the target.
• Backward pass: Gradients of the loss—numbers that indicate how to change parameters (weights or biases) to reduce error—are sent back through the network by applying the chain rule, which enables the calculation of each parameter’s contribution to the final error.
• Weights update: Parameters are adjusted to reduce the error with an optimization method (such as gradient descent) guided by the learning rate that controls the size of each update.

Backpropagation is essential in self-supervised, semi-supervised, and supervised learning, and is a core technique used to train large language models (LLMs) or other generative AI systems. Backpropagation is usually implemented through machine learning frameworks such as TensorFlow, PyTorch, and Keras, which automatically handle forward passes, gradient calculations, and weight updates.