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Neural networks learn relationships within data by detecting patterns through multiple layers of artificial neurons. These layers use weights and biases, adjusted through backpropagation and gradient descent, to improve predictions over time. Neural networks excel at learning implicit relationships, especially in unstructured data like images, text, and speech.
In contrast, relational learning in traditional AI explicitly models relationships between entities using structured representations, such as graphs or relational databases. This approach is often rule-based, leveraging predefined logical relationships rather than discovering patterns through statistical learning. While neural networks are powerful for pattern recognition, relational learning is more interpretable and suitable for reasoning tasks where structured relationships are crucial (e.g., knowledge graphs, recommendation systems).
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