Which of the following is NOT a typical use case for RNNs?Text generationSpeech recognitionImage classificationTime series predictionNone of the given options

What application(s) is(are) suitable for RNNs?1 pointSpeech RecognitionNatural Language ProcessingVideo context retrieverEstimating temperatures from weather dataAll of the above

In NLP, what does RNNs help to predict?Next song noteNext imageNext video frameNext wordNone of the options given

Question 2What is NOT TRUE about RNNs?1 pointRNNs are VERY suitable for sequential data.RNNs need to keep track of states, which is computationally expensive. RNNs are very robust against vanishing gradient problem.

In the context of natural language processing, how are RNNs typically utilized for machine translation?As a replacement for CNNsEncoding the input sequence and decoding the output sequenceAs discriminators in GANsFor image classificationFor clustering text data

For neural machine translation we typically use encoder decoder architecture. Which of the following statements are correct regarding the disadvantages of RNN/LSTM/GRU variants of encoder decoder architecture?Question 3Answera.None of theseb.For long sentence it give good accuracy and computationally expensivec.Input sequence can be larged.Vanishing gradient problem