Neural networks are computing systems inspired by the human brain. They consist of layers of interconnected nodes that process information. Each connection has a weight that adjusts during training, allowing the network to learn patterns from data.
Training a neural network involves feeding it data and adjusting weights to minimize errors. This process, called backpropagation, computes gradients and updates each weight step by step. Over many iterations, the network learns to make accurate predictions.