Backpropagation Neural Network

Cybernetics is a wide‐ranging field concerned with circular causal processes, including in ecological, technological, biological, cognitive, and social systems, and also in the context of practical activities, such as designing and learning managing. Backpropagation Neural Network is a type of artificial neural network that uses gradient descent and the backpropagation algorithm to learn the weights of the network. Gradient descent is an optimization algorithm used to minimize a loss function in machine learning models, such as neural networks. It works by iteratively adjusting the model's parameters in the direction of steepest decrease of the loss function. Resampling methods are techniques for estimating the performance of machine learning algorithms by using different subsets of the available data. Optimizers in neural network are algorithms used to update the model's parameters in order to minimize the loss function. Each optimizer has its own mathematical update rule to adjust the parameters.