Rectified Linear Units

Rectified Linear Units (ReLUs) are a type of function used in artificial neural networks to help them learn from data. They work by passing input values directly if they are positive, and turning negative inputs into zero. This simple rule makes the network's training more efficient and helps it learn more complex patterns. Essentially, ReLUs act as filters that activate only when certain conditions are met, enabling the network to model non-linear relationships without adding much computational complexity. They are widely used because of their effectiveness and ease of optimization in deep learning models.