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From Static to Contextual Word Embeddings: The Idea Behind Attention (Ep.01.02)
Episode 01.01 ended by asking what it would take for a word’s vector to be computed fresh, for a specific sentence, using the words actually nearby right now — rather…
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Subword Embeddings and fastText: Solving the OOV Problem at the Vector Level (Ep.01.01)
Episode 01.00 ended by asking: does Module 00’s word-embedding machinery just apply directly to subword tokens like lowe + r + i + n + g, or does something break?…
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Byte-Pair Encoding Explained: Why Tokenization Isn’t Just Splitting on Spaces (Ep.01.00)
Every episode of Module 00 quietly relied on .split() — text broken into whole words at whitespace, no questions asked. It was a deliberate simplification, flagged twice and never resolved.…
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Dense Word Embeddings Explained: From Co-occurrence Matrices to word2vec (Episode 00.03)
The last episode 00.02 left a real problem on the table: co-occurrence vectors give words meaningful, graded similarity — but they’re the same size as the vocabulary, mostly zeros, and…
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How Does a Word Become a Number? The Representation Problem in AI (Episode 00.02)
Episode 00.01 landed on a working definition of intelligence built around selecting good actions under uncertainty, and generalizing efficiently. Every example we used to demonstrate that — the lookup table,…
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Recent Posts
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