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What is vanishing gradient problem?
Vanishing gradients is a particular problem with recurrent neural networks as the update of the network involves unrolling the network for each input time step, in effect creating a very deep network that requires weight updates.
What is gradient vanishing and exploding problem?
Why do the gradients even vanish/explode? Now the gradients can accumulate during an update and result in very large gradients which eventually results in large updates to the network weights and leads to an unstable network. The parameters can sometimes become so large that they overflow and result in NaN values.
What is the vanishing gradient problem in recurrent neural networks?
It describes the situation where a deep multilayer feed-forward network or a recurrent neural network is unable to propagate useful gradient information from the output end of the model back to the layers near the input end of the model.
Is there a way to overcome the vanishing gradient problem?
Truncated Backpropagation Through Time (Truncated BPTT). The following “trick” tries to overcome the vanishing gradient problem by considering a moving window through the training process.
Which is faster truncated BPTT or simple BPTT?
The Truncated BPTT is much faster than the simple BPTT, and also less complex because we don’t make the contribution of the gradients from faraway steps. The minus of this approach is that dependencies of longer than the chunk length, are not taught during the training process. Another disadvantage is the detection of the vanishing gradients.
What is the problem of vanishing gradients in neural networks?
This problem makes it hard to learn and tune the parameters of the earlier layers in the network. The vanishing gradients problem is one example of unstable behaviour that you may encounter when training a deep neural network.
What’s the difference between backpropagation through time and bpTT?
The key difference is that we sum up the gradients for at each time step. In a traditional NN we don’t share parameters across layers, so we don’t need to sum anything. But in my opinion BPTT is just a fancy name for standard backpropagation on an unrolled RNN.