Rectified Linear Unit (ReLU)
Rectified Linear Unit (ReLU) is a type of activation function used in neural networks, particularly in deep learning models. It is defined as the positive part of its argument:
ReLU(x)=max(0,x), effectively setting all negative values in the input to zero.
Why ReLU Matters
ReLU is favored in many neural networks due to its simplicity and efficiency. It introduces non-linearity into the model without affecting the scalability and speed of convergence during training. This function helps the network overcome issues like the vanishing gradient problem
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