Importance of Non Linear Activation Functions in Multi Layer Perceptron

Why is non linear activation necessary for a multi layer perceptron?

Choose the correct answer:

a. To introduce linearity into the network

b. To learn and represent complex patterns and relationships in the data

c. To limit the ability of the network

Answer:

b. To learn and represent complex patterns and relationships in the data

Non linear activation functions are essential in a multi layer perceptron (MLP) to introduce non-linearity into the network, allowing it to learn and represent complex patterns and relationships in the data.

In a multi layer perceptron (MLP), non linear activation functions are crucial to introduce non-linearity into the network. Without non linear activation functions, the MLP would essentially reduce to a linear model, limiting its ability to capture and model complex data patterns.

Non linear activation functions enable the MLP to approximate non-linear functions, making it a powerful tool for tasks such as classification, regression, and pattern recognition. By applying non linear activation functions to the outputs of each neuron in the MLP, the network becomes capable of representing and learning complex relationships between inputs and outputs.

For example, in image classification tasks, non linear activation functions allow the network to learn and recognize intricate patterns and features in images, such as edges, textures, and shapes. Without non linear activation functions, the MLP would only be able to learn linear relationships between input pixels and output classes, severely limiting its ability to accurately classify images.

Therefore, the importance of non linear activation functions in a multi layer perceptron cannot be overstated as they enable the network to learn and represent complex patterns and relationships in the data.