Gradients are used in neural networks to update the model’s weights during the training process. The gradient is the partial derivative of the loss function with respect to each weight, indicating the direction and magnitude of the change needed to minimize the loss.
During backpropagation, the gradient is calculated for each layer, and the weights are updated using optimization algorithms such as stochastic gradient descent (SGD) or Adam. This allows the network to learn by adjusting its weights in a way that reduces the error between its predictions and the true outputs.
The gradient plays a key role in guiding the model toward a better solution. By using gradients, the model iteratively improves its performance by learning from the error it makes in each training iteration.