Neural Networks

Neural networks are computational models loosely inspired by the structure of biological neurons, consisting of layers of interconnected nodes (neurons) that transform input data through sequences of weighted operations and nonlinear activation functions. During training, these weights are adjusted via backpropagation to minimize prediction error. Deep neural networks — those with many layers — are the foundation of modern AI, powering image recognition, natural language processing, speech synthesis, and most other AI capabilities in production use today.

Neural networks underpin virtually every AI capability deployed in modern commerce platforms: the embedding models that power semantic search, the transformer architectures behind LLMs, the convolutional networks used in image-based product classification, and the reinforcement learning systems used in dynamic pricing. Understanding neural network fundamentals — including concepts like overfitting, training data requirements, and the role of architecture choices — helps business and technical leaders make more informed decisions about AI system design, vendor selection, and realistic capability expectations.