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

The video tutorial covers importing necessary packages like Numpy and Torch, defining input and output strings, setting the number of features and vocabulary size, computing random embeddings, and creating a function for one-hot encoding. The tutorial is a step-by-step guide to setting up a basic model structure, focusing on the initial stages of data preparation and encoding.

Which package is imported for automatic differentiation in the video?

• Pandas

• Numpy

• Torch

• SIS

What is the input string defined in the video?

• 100, 80, 5, 1

• 245, 3055, 30, 55, 10

• 45, 30, 10, 1

• 10, 20, 30, 40

What is the length of the input string in the video?

• 3

• 4

• 5

• 6

How many features are defined for the embedding space?

• 80

• 120

• 50

• 100

What is the vocabulary size mentioned in the video?

• 120

• 100

• 80

• 50

What is the shape of the embedding vector for each index?

• 120 by 1

• 100 by 1

• 80 by 1

• 50 by 1

What is the purpose of the function 'get_one_hot' defined in the video?

• To define input strings

• To generate one-hot encoded vectors

• To import necessary packages

• To compute random embeddings