Machine Learning Basics [ML 2] Making Sense Of Embeddings When you search on Amazon for “running shoes,” the system doesn’t just look for those exact words…
Privacy Tech [PET 1.b] Privacy Enhancing Technologies (PETs) — Part 2 Secure Collaboration Without Sharing Raw Data In Part 1, we covered how individual organizations protect data…
Model Intuition [MI 3] Seq2Seq Models: Basics behind LLMs When you use Google Translate to turn a complex English sentence into Spanish, or when you ask Gemini to summarize a…
Machine Learning Basics [ML 2.c] Needle in the Haystack: Embedding Training and Context Rot You’ve probably experienced this: you paste a 50-page document into ChatGPT or Claude, ask a specific question…
Machine Learning Basics How Smart Vector Search Works In the ever-evolving world, the art of forging genuine connections remains timeless. Whether it’s with colleagues,…
Machine Learning Basics [ML 2.a] Word2Vec: Start of Dense Embeddings When you type a search query into Google or ask Spotify to find “chill acoustic covers,” the system…
How Smart Vector Search Works By Archit Sharma 4 Min Read In the ever-evolving world, the art of forging genuine connections remains timeless. Whether it’s with colleagues, clients, or partners, establishing a genuine rapport paves the way for collaborative success. Read More