RNN

A modern and abstract illustration representing Recurrent Neural Networks, featuring neural network structures, connections, and an emphasis on dynamism and learning.

RNN Mastery Quiz

Test your knowledge on Recurrent Neural Networks (RNNs) and deep learning concepts with our engaging quiz! This quiz consists of 10 carefully crafted multiple-choice questions that cover key topics in RNNs, architectures, and challenges faced during training.

Assess your understanding of RNNs and their applications.
Learn about common problems like exploding gradients.
Expand your knowledge on LSTM and other advanced architectures.

10 Questions2 MinutesCreated by LearningNode247

The difference between deep learning and machine learning algorithms is that there is no need of feature engineering in machine learning algorithms, whereas, it is recommended to do feature engineering first and then apply deep learning.

True

False

Which of the following is a representation learning algorithm?

Neural network

Random Forest

K-Nearest neighbor

None of the above

Which of the following options can be used to reduce overfitting in deep learning models?

Given an n-character word, we want to predict which character would be the n+1th character in the sequence. For example, our input is “predictio” (which is a 9 character word) and we have to predict what would be the 10th character. Which neural network architecture would be suitable to complete this task?

Fully-Connected Neural Network

Convolutional Neural Network

Recurrent Neural Network

Restricted Boltzmann Machine

Exploding gradient problem is an issue in training deep networks where the gradient getS so large that the loss goes to an infinitely high value and then explodes. What is the probable approach when dealing with “Exploding Gradient” problem in RNNs?

Use modified architectures like LSTM and GRUs

Gradient clipping

Dropout

None of these

Which of the following is correct? 1 Dropout randomly masks the input weights to a neuron 2 Dropconnect randomly masks both input and output weights to a neuron

1 is True and 2 is False

1 is False and 2 is True

Both 1 and 2 are True

Both 1 and 2 are False

A recurrent neural network can be unfolded into a full-connected neural network with infinite length.

True

False

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?

Vanishing gradient problem.

Exploding gradient problem.

Suppose you are training a LSTM. You have a 10000 word vocabulary, and are using an LSTM with 100-dimensional activations a< t > . What is the dimension of Γu at each time step?

100

1000

Suppose your training examples are sentences (sequences of words). Which of the following refers to the jth word in the ith training example?

X(i)< j >

X(j)< I >

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