Why is the Rectified Linear Unit (ReLu) useful?

Why is the Rectified Linear Unit (ReLu) useful?




ReLu can let big numbers pass through, making a few neurons stale and they don't fire. This increases sparsity, which is good. ReLu maps input x to (0, X), map negative inputs to 0. Since it doesn't fire all the time it can be trained faster. Function is simple so computationally the least expensive. Works well for a large amount of applications. But we can stack several perceptrons to learn more complex functions.

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