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ReLU Activation Function Explained | Built In

ReLU Activation Function Explained | Built In

The rectified linear unit (ReLU) or rectifier activation function introduces the property of nonlinearity to a deep learning model and solves the vanishing gradients issue. It interprets the positive part of its argument. It is one of the most popular activation functions in deep learning.

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ReLU efficiently counters vanishing gradients but not exploding during training. In machine learning, we use feed-forward neural networks that rely on matrix multiplications at each step. we can face issues like vanishing gradients and exploding gradients. Vanishing gradients mean that the weights shrink towards zero, causing the model to forget or useless neurons. Exploding gradients mean the weights grow too large, leading to massive errors. To fix this, we use activation functions. Three popu

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