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.

What is the significance of the Universal Approximation Theorem in relation to neural networks?

Why is it beneficial to have more than one layer in a neural network?

How does increasing the depth of a neural network affect the number of neurons required?

In what ways does a layered architecture reduce the total number of weights in a neural network?

What challenges are associated with training deep neural networks?