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 defining and implementing a forward step in a recurrent neural network (RNN). It covers the necessary parameters, matrix multiplication, and conversion from Numpy to Torch. The tutorial also demonstrates how to compute activations using TanH and apply softmax to obtain outputs. Testing the forward step function with sample inputs is shown, followed by applying the forward step across multiple time steps in an RNN.

What is the primary purpose of defining parameters that require gradients in an RNN?

• To determine the number of layers

• To set learning rates

• To enable automatic differentiation

• To initialize the network

Why is it necessary to convert numpy arrays to torch tensors during the forward step computation?

• To reduce memory usage

• To ensure compatibility with torch operations

• To simplify the code

• To increase computation speed

What activation function is applied to compute the next activations in the RNN?

• Softmax

• Tanh

• Sigmoid

• ReLU

What is the role of the softmax function in the RNN forward step?

• To compute the next hidden state

• To update the input vector

• To initialize the weights

• To convert logits to probabilities

What is the expected shape of the output tensor after applying the softmax function?

• 100 by 1 tensor

• 81 tensor

• 10 tensor

• 50 by 1 tensor

Why is it important to test the forward step function in an RNN?

• To ensure the function runs without errors

• To verify the accuracy of the output

• To check the compatibility with other functions

• To optimize the computation time

How many times should the forward step be applied in an RNN for a given input-output pair?

• Once

• As many times as there are time steps

• As many times as there are layers

• Twice