Neural Networks

Neural networks are loosely inspired by the function of biological brains: input signals pass through several layers of nodes (neurons), each with weighted connections to neurons in the next layer. During training, these weights are adjusted so the network produces the correct outputs for the inputs.

The strength of neural networks lies in their universality. A sufficiently deep network can theoretically approximate any function, given enough data and compute. In practice, specialized architectures are more successful than generic ones: Convolutional Neural Networks for images, Recurrent Neural Networks or Transformers for sequences, Graph Neural Networks for relational data.

The AI functions in Pimcore are based on modern neural network architectures. Image tagging uses Convolutional Neural Networks, text generation Transformer models, product classification gradient boosting combined with neural features. Users do not need to understand the architectures themselves but benefit from the functions they enable, integrated into the Pimcore platform.