Archive for April 2020

Whenever people reason about probability of events, they have the tendency to consider average values between two extremes. 
In this episode I explain why such a way of approximating is wrong and dangerous, with a numerical example.

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In this episode I briefly explain the concept behind activation functions in deep learning. One of the most widely used activation function is the rectified linear unit (ReLU). 
While there are several flavors of ReLU in the literature, in this episode I speak about a very interesting approach that keeps computational complexity low while improving performance quite consistently.

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References

Dynamic ReLU https://arxiv.org/abs/2003.10027

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