Скачать или смотреть Optimizing Neural Networks

In neural network training, updating weights efficiently is crucial. Among various optimization algorithms, Newton's Method stands out for its utilization of both the direction of the negative gradient and the Hessian matrix. This method aims to find the minimum of a function by iteratively updating the weights based on these factors. Unlike gradient descent, which considers only the gradient, Newton's Method provides a more precise direction for weight updates, resulting in potentially faster convergence. However, computing and inverting the Hessian matrix can be computationally expensive, making it less practical for large-scale networks. Thus, while powerful, Newton's Method may not always be the most efficient choice depending on the specific neural network architecture and training data.