Two Layer Neural Network

Two-layer neural networks serve as a fundamental model for understanding the behavior of deeper networks, with research focusing on their optimization dynamics, generalization capabilities, and feature learning properties. Current investigations utilize stochastic gradient descent and related algorithms, often within the context of the neural tangent kernel approximation, to analyze convergence rates and the impact of hyperparameters like learning rate and network width. These studies provide crucial insights into the theoretical foundations of deep learning, informing the design of more efficient and robust algorithms and offering a clearer understanding of phenomena like spectral bias and the emergence of skills during training.