Back Propagation

Backpropagation is a fundamental algorithm for training artificial neural networks, primarily used to calculate gradients for updating network weights to minimize error. Current research focuses on improving backpropagation's efficiency and biological plausibility, exploring alternatives like forward-forward algorithms and methods that avoid the need for storing activations or full gradient calculations, often within the context of specific architectures such as transformers, spiking neural networks, and physics-informed neural networks. These efforts aim to reduce computational costs, memory requirements, and energy consumption, ultimately impacting the scalability and applicability of deep learning across various domains, including resource-constrained devices and large-scale models.