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목록deep-learning (1)
Happy Sisyphe
Why Neural Networks Can Approximate Any Function
Universal Approximation TheoremTheoremLet $f: \mathbb{R}^n \to \mathbb{R}$ be a continuous function defined on a compact domain $D \subseteq \mathbb{R}^n$. For any $\epsilon > 0$, there exists a feedforward neural network with a single hidden layer, using a nonlinear, continuous activation function $\phi: \mathbb{R} \to \mathbb{R}$, such that:$$\sup_{x \in D} |f(x) - \hat{f}(x)| $$where $\hat{f}..
Programming/ML&DL
2024. 12. 17. 15:29