Neural Networks with R

Gradient descent

Gradient descent is an iterative approach for error correction in any learning model. For neural networks during backpropagation, the process of iterating the update of weights and biases with the error times derivative of the activation function is the gradient descent approach. The steepest descent step size is replaced by a similar size from the previous step. Gradient is basically defined as the slope of the curve and is the derivative of the activation function:

The objective of deriving gradient descent at each step is to find the global cost minimum, where the error is the lowest. And this is where the model has a good ...