A neural network is a type of AI model loosely inspired by the structure of the brain, consisting of layers of interconnected nodes that process and transform data to learn patterns.
Neural networks learn by adjusting the strength of connections between nodes based on training examples. The more layers a network has, the more complex the patterns it can learn — this is the basis of deep learning.
The “brain-inspired” description is a loose analogy. Neural networks do not think, feel, or understand. They are mathematical structures that transform numerical inputs into outputs through learned parameters.