Which is the key idea of LSTM RNN backpropagation?

Which is the key idea of LSTM RNN backpropagation?

I think it makes sense to talk about an ordinary RNN first (because LSTM diagram is particularly confusing) and understand its backpropagation. When it comes to backpropagation, the key idea is network unrolling, which is way to transform the recursion in RNN into a feed-forward sequence (like on the picture above).

What is the residual error of blood pressure?

With a residual error of 12 mmHg, this person has a 68% chance of having his true SBP between 108 and 132 mmHg. Moreover, if the mean of SBP in our sample is 130 mmHg for example, then: 12 mmHg ÷ 130 mmHg = 9.2% So we can also say that the BMI accurately predicts systolic blood pressure with a percentage error of 9.2%.

What is the residual standard deviation of SBP?

SBP = β 0 + β 1 ×BMI = 100 + 1 × 20 = 120 mmHg. With a residual error of 12 mmHg, this person has a 68% chance of having his true SBP between 108 and 132 mmHg. Moreover, if the mean of SBP in our sample is 130 mmHg for example, then:

How to apply the gradient of softmax in backpropagation?

But on backprop, it seems to only do the derivative of cross entropy and not of softmax. Softmax is left as such. Shouldn’t it also take the derivative of softmax with respect to the input to softmax?

Which is the key idea when it comes to backpropagation?

When it comes to backpropagation, the key idea is network unrolling, which is way to transform the recursion in RNN into a feed-forward sequence (like on the picture above). Note that abstract RNN is eternal (can be arbitrarily large), but each particular implementation is limited because the memory is limited.

How to decipher backpropagation through any neural network?

We want to give you general principles for deciphering backpropagation through any neural network. Then apply those principles to LSTM (Long-Short Term Memory) RNNs (Recurrent Neural Networks). Let’s start with the notes we took to figure this stuff out.

How are LSTMs designed to avoid the long term dependency problem?

LSTMs are explicitly designed to avoid the long-term dependency problem. Remembering information for long periods of time is practically their default behavior, not something they struggle to learn! All recurrent neural networks have the form of a chain of repeating modules of neural network.