The Vanishing Gradient

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.

In this episode, Kyle mentions the paper Scaling to Very Very Large Corpora for Natural Language Processing.