AI Through the Lens of Topology: A Geometric Interpretation of Deep Learning
This article explains deep learning from a topological perspective, arguing that neural networks are essentially topological transformations of data in high-dimensional spaces. Through matrix multiplication and activation functions, neural networks stretch, bend, and deform data to achieve data classification and transformation. The author further points out that the training process of advanced AI models is essentially about finding the optimal topological structure in high-dimensional space, making the data more semantically relevant, and ultimately achieving inference and decision-making. This article presents a novel viewpoint that the inference process of AI can be viewed as navigation in a high-dimensional topological space.
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