Which of the following describes a neural network without an activation function?

Which of the following describes a neural network without an activation function?
A . A form of a linear regression
B . A form of a quantile regression
C . An unsupervised learning technique
D . A radial basis function kernel

View Answer

Answer: A

Explanation:

A neural network without an activation function is equivalent to a form of a linear regression. A neural network is a computational model that consists of layers of interconnected nodes (neurons) that process inputs and produce outputs. An activation function is a function that determines the output of a neuron based on its input. An activation function can introduce non-linearity into a neural network, which allows it to model complex and non-linear relationships between inputs and outputs. Without an activation function, a neural network becomes a linear combination of inputs and weights, which is essentially a linear regression model.