AI, LLM Training, and Operations Efficiency

Non-Linearity: Despite its simplicity, ReLU introduces non-linearity into the model, allowing neural networks to learn complex patterns.
Sparsity: ReLU can lead to sparse activations, as it outputs zero for any negative input. This sparsity can make the network more efficient.
Computational Efficiency: ReLU is computationally efficient since it involves simple thresholding at zero.
Gradient Propagation: ReLU helps mitigate the vanishing gradient problem that can occur with other activation functions like the sigmoid or tanh. The gradient is 1 for positive inputs, which helps maintain the gradient flow during backpropagation.

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