Deep Learning Model

Deep learning has reshaped the AI landscape over the past decade. Tasks that were practically unsolvable with classic programming (recognizing objects in images, translating between languages, generating natural language) are standard today. Three developments enabled it: large data sets, powerful GPUs, and mathematical advances in model architecture.

Deep learning models consist of multiple layers of artificial neurons that learn features hierarchically. Early layers recognize simple patterns (edges in images, letters in text), later layers combine them into complex concepts (objects, sentences, meanings). Known architectures are Convolutional Neural Networks (CNNs) for images, Recurrent Neural Networks (RNNs) and Transformers for sequences.

Pimcore uses deep learning models in several function areas. Image tagging, automatic classification of product data, semantic search, and AI-driven text generation are based on modern deep learning architectures. Through the Agent SDK, custom models (proprietary or open source) can be integrated into production workflows.