Tech, but with personality
AI/Platform Engineer @ SAP Labs and fresh graduate from Amrita Vishwa Vidyapeetham. Love robotics and AI. Building AI frameworks. Dopamine to me is learning something new every day. I even have an agent deployed on GCP for that.
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Kartik N V J K
The one-hot example where cat and dog sit at cosine zero, identical to cat and Tuesday, is the clearest way I've seen to motivate why embeddings had to exist. It makes the progression from bag-of-words to n-grams feel inevitable rather than arbitrary. If you extend the series, showing where static word2vec still fails, like one vector for a word with two meanings, would set up contextual embeddings really well.