Uploaded January 2025 | Updated September 2026, 2 weeks ago
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 analogy with Lego blocks.
Grokking Machine Learning Book:
manning.com/books/grokking-machine-learning
40% discount promo code: serranoyt
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 analogy with Lego blocks.
Grokking Machine Learning Book:
manning.com/books/grokking-machine-learning
40% discount promo code: serranoyt
