Intro to Dense Vectors for NLP and Vision
There is perhaps no greater component to the success of modern Natural Language Processing (NLP) technology than vector representations of language. The meteoric early 2010s rise of NLP was ignited with the introduction of word2vec by a team lead by Tomáš Mikolov in 2013.

Word2vec is one of the most iconic and earliest examples of dense vectors representing text. But since the days of word2vec, developments in representing language have advanced at ludicrous speeds.

This video will explore *why* we use dense vectors — and some of the best approaches to building dense vectors available today.

🌲 Pinecone article:
https://www.pinecone.io/learn/dense-vector-embeddings-nlp/

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00:00 Intro
01:50 Why Dense Vectors?
03:55 Word2vec and Representing Meaning
08:40 Sentence Transformers
09:58 Sentence Transformers in Python
15:08 Question-Answering
18:18 DPR in Python
29:55 Vision Transformers
33:22 OpenAI's CLIP in Python
42:49 Review and What's Next