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1.

Suppose your text corpus contains 80000 different words. Which of the below would you complete to reduce the dimensionality of the input vector to neural classifier?

Randomly select 10% of the words and ignore the rest.

Use convolutional layer before fully-connected classifier layer

Use embedding layer before fully-connected classifier layer

Select 10% of most frequently used words and ignore the rest

2.

We want to train a neural network to generate new funny words for a children's book. Which architecture can we use?

Word-level LSTM

Character-level LSTM

Word-level RNN

Character-level perceptron

3.

Recurrent neural network is called recurrent, because:

A network is applied for each input element and output from the previous application is passed to the next one

It is trained by a recurrent process

It consists of layers which include other subnetworks

4.

What is the main idea behind LSTM network architecture?

Fixed number of LSTM blocks for the whole dataset

It contains many layers of recurrent neural networks

Explicit state management with forgetting and state triggering

5.

What is the main idea of attention?

Attention assigns a weight coefficient to each word in the vocabulary to show how important it is

Attention is a network layer that uses attention matrix to see how much input states from each step affect the final result.

Attention builds global correlation matrix between all words in vocabulary, showing their co-occurrence

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