deep learning ↔ Unstructured data

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related 2.00 — strongly supporting 3 facts

Deep learning models are specifically designed to process and learn patterns from unstructured data, as evidenced by their use in large language models [1], hybrid neuro-symbolic systems [2], and entity resolution tasks [3].

Facts (3)

Sources

Building Better Agentic Systems with Neuro-Symbolic AI cutter.com Cutter Consortium 1 fact

claimDeep learning neural network-based large language models, such as GPT-4, Claude, and Gemini, process unstructured data including text, images, video, and streaming sensor data to learn patterns, classify data, and make predictions.

Unlocking the Potential of Generative AI through Neuro-Symbolic ... arxiv.org arXiv 1 fact

claimNeuro-symbolic artificial intelligence (NSAI) is defined as a hybrid approach that combines deep learning's ability to process large-scale, unstructured data with the structured reasoning capabilities of symbolic methods.

Construction of Knowledge Graphs: State and Challenges - arXiv arxiv.org arXiv 1 fact

claimWhile entity resolution typically operates on semi-structured data, deep learning-based approaches have been developed to address entity resolution in unstructured data sources.