Respuesta :
In building a neural network, gradient descent is typically used to update the weights of the network during the training process.
In order to reduce the error between the expected output and the actual labels, the neural network's weights are adjusted during training by feeding it a set of input data and labels. This is accomplished by using an optimization algorithm like gradient descent, which iteratively modifies the network weights to reduce error.
The error between the expected output and the true labels must be measured by a loss function before gradient descent can be computed. The gradient of the loss with respect to the weights of the neural network is then calculated using the loss function. The gradient shows us which way the weights should be changed to lessen the loss.
The magnitude of the weight update is then determined by the learning rate and the gradient used to update the network's weights. The Neural network weights are thought to be optimal for the given training data when this approach is repeated until the loss function reaches a minimum.
In conclusion, after feeding the input data and labels to the network, gradient descent is often employed to update the weights of the neural network during training. The gradient of the loss function with respect to the weights, calculated using gradient descent, and a learning rate are used to update the weights. Up until the loss function achieves a minimum, this process is repeated.
To know more about neural network kindly visit
https://brainly.com/question/14632443
#SPJ4
