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.

What packages are imported for the neural network implementation?

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How is the input string defined in the example?

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What is the size of the vocabulary defined in the example?

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Describe how the embeddings are computed in the example.

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What is the purpose of the function defined to get one hot vectors?

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Explain the significance of the one hot encoding in the context of the example.

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What will be the next steps after defining the one hot encoding function?

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