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From NLP to LLMs: the mathematical and architectural path to GPT-3.5 - #35
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Covers TF-IDF/embeddings, RNN/LSTM, seq2seq attention, self-attention, multi-head attention, the Transformer block, encoder/decoder variants, causal masking, autoregressive pretraining, GPT, scaling laws, instruction tuning, RLHF, and the GPT-3 -> GPT-3.5 -> ChatGPT lineage. Closes #31 Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01XTLzS9CmzyKDFKwHf4Fbgv
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Summary
docs/knowledge/machine-learning/from-nlp-to-llms-gpt-3-5.md, a rigorous, step-by-steparticle tracing the path from classical NLP (TF-IDF, embeddings) through RNN/LSTM, seq2seq
attention, self-attention/multi-head attention, the Transformer block, encoder/decoder
variants, causal masking, autoregressive pretraining, GPT, scaling laws (GPT-2 → GPT-3),
instruction tuning, and RLHF, ending with the GPT-3 → GPT-3.5 → ChatGPT lineage.
docs/knowledge/machine-learning/index.md.Test plan
pre-commit(trailing-whitespace, end-of-file-fixer, markdownlint) passes on the changed files.mkdocs build --strictsucceeds with no warnings.Closes #31
🤖 Generated with Claude Code
https://claude.ai/code/session_01XTLzS9CmzyKDFKwHf4Fbgv
Generated by Claude Code