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.

What does each neuron represent in a two-dimensional input space?

• A point

• A line

• A square

• A circle

How are data points classified into different classes using neurons?

• By their color

• By their distance from the origin

• By their position relative to lines

• By their size

What happens to the decision boundary as more neurons are added?

• It disappears

• It becomes smoother

• It becomes more jagged

• It becomes less accurate

What is the role of neuron connections in defining the decision boundary?

• They create noise

• They define a piecewise linear boundary

• They remove data points

• They change the input space dimensions

In a three-dimensional input space, what does each neuron represent?

• A line

• A point

• A plane

• A cube

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

• By using fewer neurons

• By using more neurons and their intersections

• By reducing the input space dimensions

• By ignoring hyperplanes

What is the effect of having more neurons in a neural network?

• It simplifies the decision boundary

• It reduces the number of hyperplanes

• It allows for a more complex decision boundary

• It makes the network slower