What is activation function?
A mathematical function that introduces non-linearity into a neural network model
activation function explained in plain English
An activation function is a crucial component in artificial neural networks, as it allows the model to learn and represent more complex relationships between inputs and outputs. It takes the output of a neural network layer and applies a transformation to it, which enables the model to introduce non-linearity and make more accurate predictions.
Analogy
Think of an activation function like a light switch. Just as a light switch can be either on or off, an activation function can 'turn on' or 'turn off' certain neurons in a neural network, allowing the model to make more nuanced decisions.
Example
Self-driving cars use activation functions in their neural networks to detect and respond to objects on the road, such as pedestrians or other vehicles.
How is activation function used?
Activation functions are used in the hidden layers of a neural network to introduce non-linearity, and in the output layer to produce the final prediction. Common examples include sigmoid, ReLU, and tanh functions.
Common misconceptions about activation function
A common misconception is that activation functions are only used in the output layer of a neural network. However, they are also used in the hidden layers to introduce non-linearity and allow the model to learn more complex relationships.
History
The concept of activation functions dates back to the 1940s, when Warren McCulloch and Walter Pitts introduced the idea of artificial neural networks. Since then, various activation functions have been developed and widely used in deep learning models.
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