Posted in data science, statistics on Apr 19th, 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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Posted in data science, Artificial Intelligence, Deep Learning on Apr 1st, 2020
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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Dynamic ReLU https://arxiv.org/abs/2003.10027
