What are Word Embedding Algorithms and why are they Useful?

Word Embedding algorithms help create more meaningful vector representations for a word in a vocabulary. To train any ML model we need to have inputs as numbers. The input for NLP models is text (words), so we need to have a way to represent this data in numbers. The easiest way to represent a word in a vocabulary, so that we can train language models (for use cases like sentiment classification, language translation) is a one hot vector representation. But using this representation implies that no word is in any…

Sneha Ghantasala

Deep Learning

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