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Activate deep learning neurons faster with Dynamic RELU (ep. 101)
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.
This episode is supported by pryml.io. At pryml we let companies share confidential data. Visit our website.
Don't forget to join us on discord channel to propose new episode or discuss the previous ones.
References
Dynamic ReLU https://arxiv.org/abs/2003.10027
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By Francesco Gadaleta
4.2
7272 ratings
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.
This episode is supported by pryml.io. At pryml we let companies share confidential data. Visit our website.
Don't forget to join us on discord channel to propose new episode or discuss the previous ones.
References
Dynamic ReLU https://arxiv.org/abs/2003.10027
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