Data Science and Machine Learning (Theory and Projects) A to Z - Sentiment Classification using RNN: RNN Setup 1

The video tutorial discusses handling large data sets by creating a smaller subset for a proof of concept. It explains building a vocabulary from this subset and setting up model parameters for a recurrent neural network. The tutorial also covers forward propagation and training, emphasizing reusing code from previous modules.

What is the main reason for using a smaller data set in this context?

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How many positive and negative examples are selected for the proof of concept?

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What is the cutoff value mentioned for building the vocabulary?

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What is the total number of tokens in the vocabulary?

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What are the hidden units defined in the context of the neural network?

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What is the significance of defining gradients in the context of the neural network?

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What is the purpose of reusing code from the previous module?

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