Deep Learning CNN Convolutional Neural Networks with Python - Universal Approximation Theorem

The video discusses the representation power of deep neural networks, focusing on their ability to model complex decision boundaries. It uses a binary classification example to illustrate how neural networks can represent intricate boundaries. The universal approximation theorem is introduced, explaining that even simple neural networks with a single hidden layer can model almost any function under certain assumptions. The video also highlights the role of architecture in determining the representation power and concludes by hinting at the necessity of depth in neural networks, which will be explored in the next video.

What are the reasons for the popularity of deep neural networks today?

How can deep neural networks model complex decision boundaries?

Discuss the importance of depth in neural networks as mentioned in the video.