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Universal Approximation Theorem - The Fundamental Building Block of Deep Learning
The Universal Approximation Theorem is the most fundamental theorem in deep learning. It says that any continuous function can be approximated, as closely as we want, by a neural networks of only one hidden layer (this layer may be huge). In this video, we see a very simple explanation of why the Universal Approximation Theorem works, using an ...
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