Sentence Transformers - EXPLAINED!

Let's talk about Recurrent Networks, Transformer Neural Networks, BERT Networks and Sentence Transformers all in one video! Follow me on M E D I U M: https://towardsdatascience.com/likelihood-probability-and-the-math-you-should-know-9bf66db5241b Joins us on D I S C O R D: https://discord.gg/3C6fKZ3E5m Please like and S U B S C R I B E: https://www.youtube.com/c/CodeEmporium/sub_confirmation=1 INVESTING [1] Webull (You can get 3 free stocks setting up a webull account today): https://a.webull.com/8XVa1znjYxio6ESdff REFERENCES [1] The Main paper that introduced Sentence Transformers: https://arxiv.org/pdf/1908.10084.pdf [2] The paper that introduced BERT: https://arxiv.org/pdf/1810.04805.pdf [3] How BERT is different from transformers: https://ai.stackexchange.com/questions/23221/how-is-bert-different-from-the-original-transformer-architecture [4] The triplet loss and architecture described in page 5: https://openreview.net/pdf?id=hga6dk7nxFB [5] AWS K-NN elastic Search: https://docs.aws.amazon.com/opensearch-service/latest/developerguide/knn.html [6] Approximate Nearest Neighbors algorithm used by spotify (ANNOY): https://github.com/spotify/annoy [7] Colah’s blog that very well breaks down LSTM networks: https://colah.github.io/posts/2015-08-Understanding-LSTMs/ [8] My video on LSTMs, explaining what you need to know: https://www.youtube.com/watch?v=QciIcRxJvsM&ab_channel=CodeEmporium [9] My original video on Transformer Neural Networks: https://www.youtube.com/watch?v=TQQlZhbC5ps