What is ReLU?
A type of activation function used in neural networks
Stands for: Rectified Linear Unit
ReLU explained in plain English
ReLU is a mathematical function that helps neural networks learn and make decisions by introducing non-linearity into the model, allowing it to learn more complex relationships between inputs and outputs
Analogy
ReLU is like a light switch, it's off (0) for negative inputs and on (positive value) for positive inputs, helping the neural network to focus on the important features
Example
Image classification models use ReLU to help the model learn to recognize objects in images by highlighting the important features
How is ReLU used?
ReLU is used in the hidden layers of a neural network to introduce non-linearity, allowing the model to learn and represent more complex relationships between inputs and outputs
Common misconceptions about ReLU
Some people think ReLU is the only activation function used in neural networks, but there are many others like sigmoid and tanh
History
ReLU was introduced in 2011 by Vinod Nair and Geoffrey Hinton as a way to improve the performance of deep neural networks
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