Gradient Backpropagation

Gradient backpropagation is a fundamental algorithm for training artificial neural networks, aiming to efficiently adjust network parameters to minimize prediction errors. Current research focuses on improving its stability and efficiency, exploring alternatives like forward-forward algorithms and investigating methods to mitigate issues such as vanishing/exploding gradients, particularly in recurrent networks and specialized architectures like binary neural networks and spiking neural networks. These advancements are crucial for enhancing the performance, interpretability, and applicability of neural networks across diverse fields, from soft sensors and image classification to physics simulations and edge computing.