Neural Network: The Dead Neuron

Neural Network: The Dead Neuron

Simple Explanation About ReLU From vanishing gradient to dead neuron. Rectified linear unit (ReLU) turns out to be the default option for the hidden layer’s activation function since it shuts down the vanishing gradient problem by having a bigger gradient than sigmoid.

Activation Function Choosing an activation function for the hidden layer is not an easy task. The configuration of the hidden layer is an extremely active topic of research, and it just doesn't have any theory about how many neurons, how many layers, and what activation function to use given a dataset. Back then, sigmoid is the most popular activation function due to its non-linearity. As time goes by, a neural network advanced to a deeper network architecture that raised the vanishing gradient problem. Rectified linear unit (ReLU) turns out to be the default option for the hidden layer’s activation function since it shuts down the vanishing gradient problem by having a bigger gradient than sigmoid.

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