Neural networks are computing systems with interconnected nodes that work much like neurons in the human brain. They rely on training data to learn and improve their accuracy over time. Neural networks simulate how the brain learns by using multiple layers of nodes (input, hidden, and output) and they're able to learn both in supervised and unsupervised situations. They can recognize hidden patterns and correlations in raw data, cluster and classify it, and, over time, continuously learn and improve. Neural networks have many applications such as image recognition, speech recognition, natural language processing, autonomous vehicles, robotics, and more.