Example Concept: A neural network consists of layers of interconnected nodes, or "neurons," where each connection has a weight that adjusts as learning progresses. The network processes input data through these layers, applying activation functions to determine the output. This structure allows the network to learn and make predictions by adjusting weights based on the error of its predictions, typically using a method called backpropagation.