Deep Learning - Recurrent Neural Networks with TensorFlow - Embeddings

The video tutorial discusses handling text data in natural language processing (NLP), focusing on the limitations of one-hot encoding due to its inefficiency and lack of meaningful geometrical structure. It introduces embedding layers as a more efficient alternative, allowing words to be represented as dense vectors with meaningful relationships. The tutorial also touches on training embeddings and the use of pre-trained vectors like Word2Vec and GloVe.

What is the relationship between the size of the vocabulary and the one hot encoded vector?

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Summarize the two main steps involved in converting words to vectors.

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How can we ensure that similar words are closer together in the embedding space?

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What are pre-trained word vectors and how are they used in embedding layers?

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