Deep Learning - Deep Neural Network for Beginners Using Python - Multi-Class Classification

The video tutorial covers binary and multiclass classification techniques. It begins with an explanation of binary classification using logistic regression, where decisions are made based on a threshold. The tutorial then transitions to multiclass classification, explaining how the output layer should have as many neurons as there are classes. The Softmax function is introduced as a method to determine the most probable class by converting raw scores into probabilities. The video emphasizes the importance of using Softmax for formal decision-making in multiclass scenarios.

What is the primary decision criterion in binary classification using logistic regression?

• The output of a sigmoid function

• The number of input features

• The number of hidden layers

• The learning rate

In multiclass classification, how is the output layer structured?

• It has no neurons

• It has twice the number of neurons as classes

• It has as many neurons as there are classes

• It has a single neuron for all classes

What does a neuron in the output layer represent in multiclass classification?

• A single class probability

• The total number of classes

• The learning rate

• The input data size

What function is used to determine the class with the highest probability in multiclass classification?

• ReLU function

• Sigmoid function

• Tanh function

• Softmax function

What is the role of the Softmax function in a neural network?

• To adjust the input size

• To increase the learning rate

• To normalize class probabilities

• To reduce the number of neurons