Neural Networks

A neural network is a machine learning architecture made of layered mathematical operations that progressively transform inputs into outputs. Each layer learns to detect increasingly abstract patterns — early layers in an image model might detect edges, later layers detect shapes, the final layer detects faces. This layered learning is what makes neural networks powerful at tasks where the relevant patterns are too complex or too numerous to specify by hand: recognizing speech, understanding language, classifying images, detecting anomalies. It's also what makes them difficult to interpret: the learned patterns exist as numeric weights across millions or billions of parameters, not as readable rules.

Neural networks are the foundation of most of the AI capabilities organizations are adopting today — large language models, image classifiers, speech recognition, fraud detection systems. Choosing to deploy a neural network-based system means accepting that the model will be harder to explain than a rule-based system and will require more data, more compute, and more evaluation effort to maintain reliably. The decision about whether that complexity is justified should be driven by whether a simpler model would actually fail the task — not by preference for the most sophisticated technology available.