Neural networks are a subset of machine learning techniques inspired by the human brain’s structure and function. They consist of interconnected layers of nodes, or neurons, that process and analyze data to identify patterns and make predictions. The architecture typically includes an input layer, one or more hidden layers, and an output layer. Neural networks excel in tasks such as image recognition, natural language processing, and speech recognition by learning from vast amounts of data through a process called training. This involves adjusting the connections between neurons to minimize prediction errors, enabling the model to improve over time.