Deep Learning - Artificial Neural Networks with Tensorflow - Multiclass Classification

The video tutorial covers multiclass classification, contrasting it with binary classification. It explains the use of the softmax function for mapping outputs to probabilities in multiclass scenarios, with examples like OCR and image recognition. The tutorial also discusses the use of activation functions in different tasks, including regression, binary, and multiclass classification, and how these apply across various neural network models.

Which of the following is NOT an example where multiclass classification is necessary?

• Image classification

• Predicting if an email is spam or not

• Speech recognition

• Optical character recognition

What is the primary purpose of the softmax function in multiclass classification?

• To convert activation values into probabilities

• To enhance the learning rate of the model

• To map input features to output labels

• To reduce the dimensionality of input data

Which property of the softmax function ensures that the output probabilities sum to one?

• The use of a linear transformation

• The exponential function in the numerator

• The sum of exponentials in the denominator

• The use of a bias term

In TensorFlow, how is the softmax function applied to a model?

• By implementing it manually in the code

• By passing the string 'softmax' as an argument

• By using a separate library for activation functions

• By setting a specific parameter in the optimizer

Which activation function is used for binary classification tasks?

• ReLU

• Softmax

• Tanh

• Sigmoid

What is the activation function used for regression tasks?

• ReLU function

• Identity function

• Softmax function

• Sigmoid function

Why is the softmax function considered more general than the sigmoid function?

• It is faster to compute than the sigmoid function

• It can be used in any type of neural network architecture

• It can handle both regression and classification tasks

• It can handle multiclass classification, including binary classification