I Built LLM Explained: 43 Interactive Pages That Take You From Tokens to AI Agents

说明与用途

作者创建了 LLM Explained，一套 43 页的交互式课程，通过动画、可切换深度解释和实操小部件教授大型语言模型基础知识。

问题

Learners struggle to understand large language model concepts because new terminology and complex equations appear rapidly, making it hard to grasp why certain design choices (e.g., scaling by √d_k) are made.

结果

The course enables learners to understand LLM concepts through visual, interactive, and hands‑on methods, reducing confusion around terminology and mathematical details.

工作流

• 1. Visit the curriculum map to see the course structure.
• 2. Choose a module and select a depth level (Intuition, Mechanics, Maths).
• 3. Scroll to trigger step‑by‑step animations and use play/pause controls.
• 4. Interact with widgets (e.g., toggle √d_k, parameter counter, BPE tokenizer).
• 5. Run optional in‑browser Python cells to reproduce figures.
• 6. Read the “what this picture leaves out” box for honest limits.

证据

I built the resource I wished I had: LLM Explained. It’s a free, interactive course that goes from "why did RNNs hit a wall?" all the way to "how does a production agent retrieve, reason, act and get evaluated?"

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Every concept appears as a picture you can play with before you see its equation.

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Every module has at least one widget you control.

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Every animation also has a text transcript, and if your system has reduced motion turned on, animations turn into numbered static frames.

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