Thinking with AI: Master Any Algorithm By Asking These Questions

 🧠 PHASE 1: Intuition & Purpose

1. What problem does this algorithm solve?

(e.g., "Why was Word2Vec needed instead of one-hot vectors?")

2. What are the key ideas or intuitions behind it?

(e.g., "How does it learn meaning from context?")

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🧮 PHASE 2: Math & Mechanics

3. What are the inputs and outputs of the algorithm?

(e.g., "What do I give it? What do I get back?")

4. What are the main steps or components involved?

(e.g., "What’s the forward pass? What layers are there?")

5. What is the objective function or optimization criterion?

(e.g., "What is being minimized or maximized?")

6. What is the mathematical formulation behind each step?

(e.g., "What equations define how it works?")

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✏️ PHASE 3: Manual Calculation

7. Can I work through a simple example by hand?

(e.g., "Using just 4 data points, can I simulate one step manually?")

8. What happens during one iteration of the algorithm?

(e.g., "From input to output, how are intermediate results computed?")

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💻 PHASE 4: Code & Build

9. Can I implement the algorithm from scratch (e.g., using NumPy)?

(e.g., "Can I write my own forward pass and update rules?")

10. What is the minimal working example I can build?

(e.g., "How can I test it on 2–3 data points?")

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⚖️ PHASE 5: Evaluation & Comparison

11. How does my implementation compare with library versions?

(e.g., "Is my Word2Vec close to Gensim?")

12. What are the strengths, limitations, and assumptions?

(e.g., "Does this require a lot of data? Is it biased?")

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🧭 PHASE 6: Application & Extension

13. Where and how is this algorithm used in the real world?

(e.g., "Where does Word2Vec show up in NLP pipelines?")

14. How can I adapt or improve this algorithm for a new task?

(e.g., "Can I combine it with CNNs? Use it for image data?")

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📝 PHASE 7: Summarize & Reflect

15. Can I write a 1-page summary with equations, intuition, and use cases?

(This reinforces clarity and builds your own "cheat sheet.")

16. Can I explain the algorithm confidently to someone else?

(Teaching reveals mastery.)

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📦 BONUS QUESTIONS FOR ADVANCED LEARNERS

• What are the time and space complexities?

• What happens in edge cases (e.g., missing data, noise)?

• What papers introduced and improved this algorithm?

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✅ OUTPUT: What You’ll Achieve

By asking and answering these questions, you will:

• Grasp the intuition and use-case

• Know the mathematics

• Be able to simulate a simple example

• Implement it from scratch

• Apply and evaluate it meaningfully

• Build confidence and teaching-level understanding