Deep Learning - Crash Course 2023 - Confirming Universal Approximation Theorem Works

The video tutorial explains the universal approximation theorem, demonstrating how a single neuron with sigmoid activation struggles to learn complex data representations. By increasing the number of neurons to three, the model successfully learns the data's complex structure, illustrating the theorem's principle. This experiment highlights the intuition behind deep learning, where simple models are connected to form networks capable of learning complex data representations.

What observations can be made about the decision boundary drawn by the single neuron compared to three neurons?

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Discuss how the universal approximation theorem is validated through the training of neural networks.

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