Deep Learning CNN Convolutional Neural Networks with Python - Decision Boundary in DNN

The video tutorial explains the concept of decision boundaries in neural networks. It describes how each neuron acts as a computational unit representing a hyperplane in the input space. In a two-dimensional space, neurons represent lines that can classify data points into different classes. By increasing the number of neurons, the decision boundary can be approximated more smoothly. In higher-dimensional spaces, neurons represent hyperplanes, and their intersections form complex decision boundaries.

Describe how increasing the number of neurons affects the smoothness of the decision boundary.

What happens to the representation of a neuron when the input space has more than two dimensions?

How can complex decision boundaries be achieved in higher-dimensional spaces?