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EPISODE · Jun 30, 2017 · 15 MIN
[MINI] The Vanishing Gradient
from Data Skeptic
This episode discusses the vanishing gradient - a problem that arises when training deep neural networks in which nearly all the gradients are very close to zero by the time back-propagation has reached the first hidden layer. This makes learning virtually impossible without some clever trick or improved methodology to help earlier layers begin to learn.
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[MINI] The Vanishing Gradient
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Frequently Asked Questions
How long is this episode of Data Skeptic?
This episode is 15 minutes long.
When was this Data Skeptic episode published?
This episode was published on June 30, 2017.
What is this episode about?
This episode discusses the vanishing gradient - a problem that arises when training deep neural networks in which nearly all the gradients are very close to zero by the time back-propagation has reached the first hidden layer. This makes learning...
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