Harsh VardhanA Comprehensive Guide to Word Embeddings in NLPIn the realm of Natural Language Processing (NLP), converting words into vectors — commonly referred to as word embeddings — is…3d ago
Fabio MatricardiinArtificial Intelligence in Plain EnglishMaybe Embeddings Are What You NeedA beginner’s guide to understanding embeddings in Machine Learning and how they bridge the gap in NLP.Mar 84
Kusalani TharunyaWhat are Word Embeddings?Word embeddings are dense vector representations of words that capture their meanings, semantic relationships, and syntactic roles. Unlike…3d ago3d ago
Arun PrasadOpen AI 3'rd gen embedding models — what’s driving the improvements?The bedrock of semantic search is the vector embedding. The effectiveness of the vector embedding to capture (i) the concepts in the…Mar 21Mar 21
Harsh VardhanA Comprehensive Guide to Word Embeddings in NLPIn the realm of Natural Language Processing (NLP), converting words into vectors — commonly referred to as word embeddings — is…3d ago
Fabio MatricardiinArtificial Intelligence in Plain EnglishMaybe Embeddings Are What You NeedA beginner’s guide to understanding embeddings in Machine Learning and how they bridge the gap in NLP.Mar 84
Kusalani TharunyaWhat are Word Embeddings?Word embeddings are dense vector representations of words that capture their meanings, semantic relationships, and syntactic roles. Unlike…3d ago
Arun PrasadOpen AI 3'rd gen embedding models — what’s driving the improvements?The bedrock of semantic search is the vector embedding. The effectiveness of the vector embedding to capture (i) the concepts in the…Mar 21
JainvidipUnderstanding Word2Vec in Natural Language Processing (NLP)In the realm of Natural Language Processing (NLP), understanding the meaning and context of words is crucial for tasks such as text…Jul 7
Milana ShkhanukovaCosine distance and cosine similarity-Okay, Milana, there is a mistake: cosine similarity cannot be negative. - Oh, it can be.Mar 4, 20232
