Layer Neural Network

Single-layer neural networks, despite their simplicity, are a vibrant area of research focusing on their surprising capabilities and efficiency. Current investigations explore various activation functions (like PReLU), optimization methods (including gradient flow and stochastic gradient descent), and applications in diverse fields such as solving differential equations and federated learning. This renewed interest stems from their potential for improved computational efficiency, interpretability, and even achieving comparable accuracy to deeper networks in specific contexts, offering valuable insights into fundamental neural network properties and practical advantages in resource-constrained environments.