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.

Evaluate responses using AI:

OFF

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

Evaluate responses using AI:

OFF

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

Evaluate responses using AI:

OFF