Data Science and Machine Learning (Theory and Projects) A to Z - RNN Implementation: Language Modelling Next Word Predic

The video tutorial explains the forward step of an RNN model, focusing on how to unroll the function based on input size. It defines the full forward RNN process, detailing the use of embeddings, previous memory, and output generation. The tutorial includes testing the function for bugs and introduces the concept of a loss function for parameter updates, setting the stage for the next video.

What is the purpose of unrolling the RNN network?

• To improve the accuracy of predictions

• To decrease the computational complexity

• To handle multiple inputs by calling the function repeatedly

• To increase the number of layers in the network

What does the full forward RNN function require as inputs?

• Only the previous memory

• Only the input matrix X

• Input matrix X and output Y

• Embeddings, input matrices, and previous memory

How is the previous memory updated in the RNN?

• By using the step forward function

• By adding a constant value

• By multiplying with a random matrix

• By using the current output Y hat

What determines the number of times the step forward function is called?

• The initial value of the previous memory

• The size of the output matrix

• The number of embeddings in the input

• The number of layers in the network

What is the length of the output vector Y hat for each word?

• 10

• 80

• 5

• 8

Why might the predictions from Y hat not be accurate initially?

• Because the parameters are not yet optimized

• Due to incorrect input data

• Because the network is not deep enough

• Due to a lack of sufficient training data

What is the next step after obtaining the initial predictions from the RNN?

• Reduce the number of layers

• Change the input matrix

• Increase the number of inputs

• Define and implement a loss function