Theoretical Foundations of Neural Networks

Theoretical foundations of neural networks are based on modeling how the brain processes information. Neural networks mimic this by connecting simple units called neurons, which output signals based on inputs and learned weights. They learn by adjusting these weights to improve accuracy on tasks like recognizing images or understanding language. Mathematically, they rely on functions that transform data through layers, enabling complex pattern recognition. The theory involves understanding how these transformations approximate functions, ensuring networks can generalize from training data to new, unseen data, creating powerful tools for artificial intelligence.