Exploding gradients issue is seen in RNN.1 pointTrueFalse

Vanishing gradients is observed in RNN.1 pointTrueFalse

Why are RNNs susceptible to issues with their gradients?1 pointGradients can grow exponentiallyGradients can quickly drop and stabilize at near zeroPropagation of errors due to the recurrent characteristicNumerical computation of gradients can drive into instabilitiesAll of the above

You are training an RNN, and find that your weights and activations are all taking on the value of NaN (“Not a Number”). Which of these is the most likely cause of this problem?Question 8Answera. Exploding gradient problem.b.Vanishing gradient problem.c. ReLU activation function g(.) is used to compute g(z), where z is too large.d.  Sigmoid activation function g(.) is used to compute g(z), where z is too large.

During the training of RNNs for sequence generation, what is the common technique used to mitigate the vanishing gradient problem?DropoutGradient clippingData augmentationL1 regularizationBatch normalization

Why is exploding gradient occurred?1 pointComputing gradient wrt hidden states requires many factors of weights and repeated gradient computationComputing gradient wrt inputs requires many factors of outputs and repeated gradient computationComputing gradient wrt weights requires many factors of hidden states and repeated gradient computationNone of the above