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

The video tutorial explains the concept of vocabulary in language models, focusing on indexing unique words. It provides examples of how words are indexed and used to predict subsequent words in a sequence. The tutorial also covers the creation of input and target vectors for training models and introduces the concept of converting indices into feature vectors for computation, leading to word embeddings.

What is the total number of unique words in the vocabulary discussed in the video?

• 200

• 150

• 100

• 50

In the context of the video, what is the index of the word 'cat' in the vocabulary?

• 55

• 30

• 10

• 45

What is the primary goal of the model as described in the video?

• To sort words alphabetically

• To count the number of words

• To predict the next word in a sequence

• To translate words into another language

What does the input vector consist of in the training process?

• Paragraphs

• Sentences

• Words

• Indices

What will be covered in the next video as mentioned in the transcript?

• Building a new vocabulary

• Learning a new language

• Converting indices to feature vectors

• Creating a dictionary