MACHINE LEARNING

APPLICATION OF SUPERVISED LEARNING

DEEP LEARNING

Question [CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
Why RNN doesn’t suit long-term sequence data?
A
Because it doesn’t have enough input
B
None of DL models suit long-term sequence data
C
Due to vanishing gradient problem
D
SueSu said so
Explanation: 

Detailed explanation-1: -However, RNNs suffer from the problem of vanishing gradients, which hampers learning of long data sequences. The gradients carry information used in the RNN parameter update and when the gradient becomes smaller and smaller, the parameter updates become insignificant which means no real learning is done.

Detailed explanation-2: -Weight initialization is one technique that can be used to solve the vanishing gradient problem. It involves artificially creating an initial value for weights in a neural network to prevent the backpropagation algorithm from assigning weights that are unrealistically small.

Detailed explanation-3: -RNNs suffer from the problem of vanishing gradients. The gradients carry information used in the RNN, and when the gradient becomes too small, the parameter updates become insignificant. This makes the learning of long data sequences difficult.

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