Neural networks are computational models inspired by biological neurons, consisting of interconnected layers of nodes (neurons) that learn patterns through backpropagation and gradient descent. They form the foundation of modern deep learning, enabling breakthroughs in computer vision, natural language processing, and sequential data modeling. Key to success: understanding that network depth enables feature hierarchy, proper initialization prevents gradient issues, and regularization strategies balance model capacity with generalization.