LLMs don't think in words. I break down what happens between your prompt and the next token: tokenization, embeddings, attention, logits, and sampling. With the math, tables, and interactive demos, no math degree required.
machine learning
- Your model working in a notebook means nothing. I break down what it actually takes to deploy ML systems in production — the messy parts, the real tradeoffs, and what I wish I knew earlier.
- A practical 30-day roadmap to understand Machine Learning from first principles — covering data, models, evaluation, and real-world workflows without unnecessary math or hype.
- Recommendation systems aren’t magic — they’re layered engineering decisions disguised as AI. I break down how Netflix-style recommenders actually work, the tradeoffs nobody mentions, and how I think about building them in the real world.
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