What is the vanishing gradient problem in neural networks? How can it be corrected?

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What is the vanishing gradient problem in neural networks? How can it be corrected?

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Answer 1

Answer #1

Vanishing Gradient Problem is a problem with gradient based methods (e.g Back Propagation). In particular, this problem makes it really hard to learn and tune the parameters of the earlier layers in the network.

As a result of Vanishing Gradient, a Deep Learning model takes longer time to train and learn from the data and sometimes may not train at all and show error. This results in less or no convergence of the neural network.

Due to Vanishing Gradient, your slope becomes too small and decreases gradually to a very small value (sometimes negative).

Possible solutions are:

Long Short Term Memory Networks
Faster Hardware
Other activation functions:
Residual Networks

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