Neural networks are a type of machine learning model inspired by the structure and function of the human brain. They consist of interconnected nodes, or neurons, organized into layers. Each neuron takes in input, processes it, and passes on an output to the next layer of neurons. Neural networks are capable of learning and adapting to complex patterns in data, making them well-suited for tasks such as image recognition, natural language processing, and speech recognition. They are typically trained using a process called backpropagation, where the model adjusts its internal parameters based on the difference between its predicted output and the true output. Neural networks have seen a surge in popularity in recent years due to advancements in computational power and data availability, and have been successfully applied in a wide range of industries, including healthcare, finance, and autonomous vehicles.