Feiza Data·Nov 1Word2Vec: The Geometry Behind Similar WordsType tortilla into a grocery search bar. A helpful search result might recommend similar products like pita, naan, or flatbread. This isn’t…A response icon1A response icon1
Mathcube·Oct 19Why King − Man + Woman = Queen: What a Word Embedding Actually IsWord2vec, the 2013 method everyone called a neural network, was factorizing a word-pair table linguists had used since 1990.A response icon2A response icon2
Learner·Oct 13Word2Vec: Learning Word Meaning from ContextHello everyone 👋A response icon1A response icon1
Mehdi Samadi·Oct 2From Words to Vectors: How Machines Learn MeaningA conceptual walkthrough of Word2Vec — the model that turned language into geometry.
Jairaj Sathyanarayana·Sep 14What 36 months of Indian mutual fund holdings reveal about stocks, funds and embeddingsInspired by the ‘Asset and Investor Embeddings’ paperon 480,235 Indian fund-stock-month observations. Only active/ non-index funds were…A response icon1A response icon1
InTeam Pratilipibykovida nelakuditi·Sep 1Who’s your neighbour?How we rebuilt end-of-story recommendations at Pratilipi — 1.3 million series, 12 languages, and one three-hour outage.
Midhun G Raj·Aug 28Understanding Word2Vec From Scratch: From Context Words to BackpropagationWhen I first heard about Word2Vec, I understood the basic idea: computers cannot directly understand words, so we convert words into…
Akshat A. Mistry·Aug 7From Words to VectorsMachine Learning Foundations · A Series on “how machines come to represent meaning.”
Surisetti Vamsi krishna·Jul 27NLP from Scratch : Word2Vec, GloVe, and FastText (Teaching Machines What Words Actually Mean)This is Part 2 of the “BoW to Transformers” series. In Part 1, we built Bag of Words and TF-IDF by hand — and ended on a confession…
Nethmi Tharushika·Jul 15The 2010s: How NLP Learned to Think in VectorsIf you had to pick one idea that defined natural language processing in the 2010s, it wouldn’t be a single model , it would be a shift in…
