For any ( f \in \mathcal{F}(\mathbb{R}^n) ) and any ( \epsilon > 0 ), there exists a neural network ( \mathcal{N}(\mathbf{x}; \theta) ) with parameters ( \theta ) such that: [ \sup_{\mathbf{x} \in K} \left| f(\mathbf{x}) - \mathcal{N}(\mathbf{x}; \theta) \right| < \epsilon, ] where ( K \subset \mathbb{R}^n ) is compact.
![Cover image for Unified Approximation Theorem for Neural Networks](https://media.dev.to/cdn-cgi/image/width=1000,height=420,fit=cover,gravity=auto,format=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fx0oy2mqzz1z0qd3la6ts.jpg)
For further actions, you may consider blocking this person and/or reporting abuse
Top comments (1)
Hello everyone,
I hope you're all doing well. I recently launched an open-source project called the Ultimate JavaScript Project, and I'd love your support. Please check it out and give it a star on GitHub: Ultimate JavaScript Project. Your support would mean a lot to me and greatly help in the project's growth.
Thank you!