Data Science and Machine Learning (Theory and Projects) A to Z - Deep Neural Networks and Deep Learning Basics: Decision

The video tutorial explains the concept of decision boundaries in neural networks. It begins by introducing the idea that each neuron in a neural network acts as a computational unit representing a hyperplane in the input space. In a two-dimensional space, neurons represent lines, and these lines can be used to classify data points into different classes. The tutorial further explains how increasing the number of neurons allows for a smoother approximation of decision boundaries. In higher-dimensional spaces, neurons represent hyperplanes, and their intersections form complex decision boundaries. The video emphasizes the role of neurons in defining piecewise linear boundaries and how they can model continuous functions.

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

• A point

• A line

• A circle

• A square

How are data points classified using multiple neurons in a neural network?

• By their size

• By their position relative to the lines

• By their distance from the origin

• By their color

What effect does increasing the number of neurons have on the decision boundary?

• It makes the boundary more jagged

• It smooths the boundary

• It has no effect

• It makes the boundary disappear

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

• A point

• A plane

• A cube

• A line

What is the role of hyperplanes in higher-dimensional spaces?

• They form circles

• They create simple boundaries

• They intersect to form complex decision boundaries

• They do not play any role

How can a complex decision boundary be achieved in a neural network?

• By using a single neuron

• By using only two neurons

• By using multiple neurons and their intersections

• By using no neurons

What happens to the decision boundary when more neurons are added in a five-dimensional space?

• It becomes a straight line

• It remains unchanged

• It becomes more complex and smooth

• It becomes less complex