How do neural networks use calculus?

How do neural networks use calculus?

Neural networks use calculus primarily for optimization. During training, they adjust weights to minimize the loss function, which often involves calculating gradients using derivatives.

Can you explain what a loss function is?

A loss function measures how well the neural network's predictions match the actual outcomes. It quantifies the error, guiding the optimization process.

What role does backpropagation play in this?

Backpropagation is an algorithm used to compute the gradient of the loss function with respect to the weights. It applies the chain rule of calculus to propagate errors backward through the network.

Are there specific types of calculus used in neural networks?

Yes, primarily differential calculus is used for finding gradients, while integral calculus can be involved in understanding certain types of functions and their properties.