Description: The error gradient in the context of neural networks refers to the derivative of the error function with respect to the model parameters, such as the weights and biases of the neurons. This concept is fundamental in the training process of neural networks, as it allows for the adjustment of parameters to minimize the error in the model’s predictions. The gradient calculation is performed using the backpropagation algorithm, which employs the chain rule to propagate the error from the output layer back through the previous layers. Through this process, information is obtained about how each parameter contributes to the total error, enabling precise adjustments. The error gradient is essential for model optimization, as it guides the direction and magnitude of changes in parameters during training. In summary, the error gradient is a key tool that allows neural networks to learn from data and improve their performance over time.
