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本文从范畴论视角深入探讨迁移学习，提出深度流形理论，认为神经网络通过无属性数值计算学习关系结构，从而实现跨领域迁移，并解释了分类的内在逻辑。
This article presents a mathematical perspective on why modern neural networks accommodate diverse architectures and attention mechanisms, framing them as different implementations of constraints within a learnable numerical system.