Description: Normalized gradient is a technique used in the training of machine learning models to ensure that the gradient does not explode or vanish during the optimization process. This technique is based on the idea of adjusting the magnitude of the gradient before applying it to the model parameters, allowing for more stable and efficient learning. In the context of deep neural networks, where gradients can vary drastically in magnitude, normalized gradient helps maintain an appropriate range of values, avoiding issues that can arise from excessively large or small gradients. This is achieved by normalizing the gradient, which involves scaling it so that its length is constant, facilitating a more uniform advance in the parameter space. Implementing this technique can improve model convergence and reduce training time, which is crucial in applications dealing with large volumes of data and computational complexity. In summary, normalized gradient is a valuable tool in improving the stability and performance of the learning process, contributing to a more robust and efficient training of machine learning models.
