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?

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

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

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