Recurrent Neural Network (RNN) Quiz

What is the basic concept of Recurrent Neural Network?

• Use previous inputs to find the next output according to the training set.

• Use a loop between inputs and outputs in order to achieve the better prediction.

• Use recurrent features from dataset to find the best answers.

• Use loops between the most important features to predict next output.

According to the image, classify the type of connection we have in the example 1.

• One to one

• One to many

• Many to one

• Many to many

According to the image, classify the type of connection we have in the example 3.

• Many to one

• Many to many

• One to one

• One to many

According to the image, classify the type of connection we have in the example 4.

• Many to many

• Many to one

• One to many

• One to one

According to the image, classify the type of connection we have in the example 5.

• Many to many

• One to many

• Many to one

• One to one

What is 'gradient' when we are talking about RNN?

• A gradient is a partial derivative with respect to its inputs

• It is how RNN calls its features

• The most important step of RNN algorithm

• A parameter that can help you improve the algorithm's accuracy

One of the RNN's issue is 'Exploding Gradients'. What is that?

• When the algorithm assigns a stupidly high importance to the weights, without much reason

• When the algorithm assigns a stupidly high importance to the weights, because the better features

• When the algorithm assigns a stupidly high importance to the weights, when your dataset is too big

• When the algorithm assigns a stupidly high importance to the weights, when your data is too small

The other RNN's issue is called 'Vanishing Gradients'. What is that?

• When the values of a gradient are too small and the model stops learning or takes way too long because of that.

• When the values of a gradient are too big and the model stops learning or takes way too long because of that.

• When the values of a gradient are too small and the model joins in a loop because of that.

• When the values of a gradient are too big and the model joins in a loop because of that.

LSTM? What is that?

• LSTM networks are an extension for recurrent neural networks, which basically extends their memory. Therefore it is well suited to learn from important experiences that have very long time lags in between

• LSTM networks are an extension for recurrent neural networks, which basically extends their memory. Therefore it is well suited to learn from important experiences that have very low time lags in between

• LSTM networks are an extension for recurrent neural networks, which basically shorten their memory. Therefore it is well suited to learn from important experiences that have very low time lags in between

• LSTM networks are an extension for recurrent neural networks, which basically extends their memory. Therefore it is not recommended to use it, unless you are using a small Dataset.