Data Science and Machine Learning (Theory and Projects) A to Z - Sentiment Classification using RNN: Vectorizer

The video tutorial covers the process of vectorizing text reviews using one-hot encoding and building a vectorizer function in Python. It then tests the function with sample reviews to ensure it outputs the correct feature vectors. The tutorial concludes with an overview of a recurrent neural network (RNN) architecture, highlighting the use of tanh and sigmoid activations, and explaining how the model processes input sequences to produce output only for the last word in a review.

How does the vectorize function ensure that it returns a two-dimensional array of one hot vectors?

What does the term 'Y hat value' refer to in the context of the model's output?

Explain how the model discards output values for previous unrolls in the recurrent neural network.