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

The video tutorial explains the process of setting up weight matrices for a Recurrent Neural Network (RNN). It begins with an introduction to the concept of weight matrices and the initial state vector H0. The tutorial then details the initialization of weights using a uniform distribution and the setup of WX and WY matrices. Finally, it prepares for the forward pass computation in the RNN, emphasizing the importance of defining the process for one time step to enable full propagation.

What is the primary purpose of defining weight matrices in an RNN?

• To initialize the output layer

• To transform input data into hidden states

• To store the input data

• To calculate the loss function

What is the size of the initial history vector H0 if the number of units is 50?

• 50 by 50

• 1 by 50

• 100 by 50

• 50 by 1

How is the weight matrix WH initialized in the RNN setup?

• Using zeros

• Using random values between 0 and 1

• Using ones

• Using the identity matrix

Why is it important to set the 'requires_grad' parameter to true for weight matrices?

• To initialize weights to zero

• To enable weight sharing

• To prevent overfitting

• To allow gradient computation during backpropagation

What is the size of the WX matrix if the number of units is 50 and the number of features is 100?

• 50 by 100

• 100 by 50

• 100 by 100

• 50 by 50

What determines the size of the WY matrix in the RNN?

• The number of hidden layers

• The batch size

• The number of units and vocabulary size

• The number of input features

What is the next step after initializing the weight matrices and history vector in an RNN?

• Performing the backward pass

• Applying dropout

• Defining the forward pass for one time step

• Calculating the loss function