Optimizing Neural Networks with Automatic Differentiation

This chapter explores the optimization challenges in high-dimensional neural network parameter spaces and the critical role of gradient calculations. It highlights the benefits of automatic differentiation in enhancing computational efficiency and navigating complex function landscapes.