Deep learning

Deep learning is a subfield of machine learning that uses artificial neural networks with many layers—hence "deep"—to learn hierarchical representations directly from raw data. Each layer of the network learns to detect progressively more abstract features: in image recognition, early layers detect edges and textures, middle layers detect shapes and objects, and later layers detect high-level semantic concepts. Deep learning systems are trained end-to-end using gradient descent and backpropagation, automatically discovering the feature representations most useful for the target task rather than relying on hand-engineered features.

Deep learning is the foundation of virtually all modern AI capabilities in commerce: the convolutional neural networks that power visual search and product image classification, the transformer architectures that underlie large language models and semantic search, the recurrent and attention-based architectures that drive time-series demand forecasting, and the embedding models that represent products, customers, and queries in vector spaces for similarity matching. The practical implication for commerce enterprises is that deep learning has commoditized capabilities—like natural language understanding and image recognition—that previously required prohibitive custom development, enabling teams to build sophisticated AI features by fine-tuning pre-trained models on domain-specific data rather than training from scratch.