Deep Learning CNN Convolutional Neural Networks with Python - Why Depth

The video explains the Universal Approximation Theorem, which states that a neural network with a single layer can model almost any function given enough neurons. However, using a single layer often requires an impractical number of neurons. Layered architectures, or deep networks, reduce the number of neurons and weights needed without losing representation power. This depth provides flexibility and efficiency, though it introduces training challenges. The video concludes by emphasizing the importance of depth in neural networks despite the theorem's implications.

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