What is the purpose of the activation function in a neural network?
a) To introduce non-linearity into the model
b) To initialize the weights of the network
c) To normalize the input data
d) To compute the loss function
Answer:
a) To introduce non-linearity into the model
Explanation:
An activation function in a neural network is responsible for introducing non-linearity into the model. Without it, the network would behave like a linear model, limiting its ability to solve complex problems.
Popular activation functions include the sigmoid, tanh, and ReLU (Rectified Linear Unit). These functions transform the input data into output that is passed to the next layer, enabling the network to learn and represent complex patterns in the data.
The activation function plays a crucial role in the network’s ability to learn from the data, especially in deep learning models where multiple layers are involved.