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?

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

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

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