Neural networks are modeled after the human brain and consist of interconnected neurons that process information. A neural network has an input layer, hidden layers, and an output layer. Within each hidden layer are neurons that perform computations using weights, biases, and an activation function. The network is trained using gradient descent and backpropagation to minimize a loss function by adjusting the weights until the desired output is achieved. Popular applications of neural networks include computer vision, natural language processing, and autonomous vehicles. Deep learning uses neural networks with many hidden layers to learn complex patterns from large amounts of data.