A neural network is a computational model inspired by the human brain, designed to recognize patterns and make decisions. It consists of layers of interconnected nodes (neurons) that process input data and pass the results through activation functions.
The network learns by adjusting the weights of connections between neurons based on the error in predictions, usually using algorithms like backpropagation. These networks can be used for a wide variety of tasks, such as classification, regression, and reinforcement learning.
Neural networks can have different architectures, such as feedforward, convolutional, or recurrent, each suited for specific types of data or problems.