research reverse-transfer-learning-can-word-embeddings-trained-for-different-nlp-tasks-improve-neural-language-models 
Unite.AI
NOVEMBER 8, 2023
Natural Language Processing (NLP) has experienced some of the most impactful breakthroughs in recent years, primarily due to the the transformer architecture. The major downside of one-hot encoding is that it treats each word as an isolated entity, with no relation to other words.
Mlearning.ai
JUNE 8, 2023
To enable semantic search, we need something called embedding/vector/vector embedding. Intuitively, think when you compare if two things are similar to each other, you want to represent the two things (text, image, video, audio or ideally anything that can be digitized) as two points and then find how close they are.
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