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Activation Functions Explained: Sigmoid, ReLU, GELU & The Vanishing Gradient | Deep Learning
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Activation Functions Explained: Sigmoid, ReLU, GELU & The Vanishing Gradient | Deep Learning
If we stack thousands of layers of neurons without activation functions, what do we get? We get a single linear regression model. All that computing power is wasted. In this Deep Learning deep dive (Episode 2), we escape the "Linear Trap" and surgically dismantle the most critical component of a neural network: Activation Functions. These are ...
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