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

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

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.

Recurrent Artificial Neural NetworksRecurrent Artificial Neural Networks (RNNs) are a type of neural network architecture that is designed to handle sequential data by introducing connections between units in the network that form directed cycles. This cyclic structure allows information to persist over time and enables the network to exhibit dynamic temporal behavior.In contrast to feedforward neural networks, where information flows in one direction from input to output, RNNs have connections that loop back on themselves, allowing them to maintain an internal state or memory of previous inputs. This makes them well-suited for tasks that involve sequential data or time series, such as natural language processing, speech recognition, and time series prediction. The basic unit of an RNN is called a recurrent neuron or a recurrent unit.

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

To which of these tasks would you apply a many-to-one RNN architecture?Question 7Answera.   Both sentiment classification and gender recognition from speechb.Gender recognition from speech (input an audio clip and output a label indicating the speaker’s gender)c.   Speech recognition (input an audio clip and output a transcript)d. Sentiment classification (input a piece of text and output a 0/1 to denote positive or negative sentiment)