A.1 Recommended Learning Resources

Use outside resources only when they solve a current bottleneck. Do not collect links as a substitute for building projects.

Choose by bottleneck

If you are stuck on	Add this first	Return to
Concept intuition	3Blue1Brown, visual explainers, course diagrams	The same chapter exercise
Python basics	Python official tutorial, Real Python	Chapter 2 code practice
Data work	Pandas, NumPy, Matplotlib, SQL docs	Chapter 3 mini projects
Math and ML theory	Andrew Ng ML, Zhou Zhihua, scikit-learn docs	Chapters 4-5
Deep learning code	PyTorch docs, Dive into Deep Learning, Fast.ai	Chapter 6 notebooks
LLM usage	Hugging Face docs, model provider docs	Chapters 7-8
RAG / Agent engineering	LangChain, LangGraph, LlamaIndex, MCP docs	Chapters 8-9 projects
CV / NLP / multimodal	CS231n, CS224n, OpenCV, diffusion and multimodal docs	Chapters 10-12

One-resource rule

For each bottleneck, choose one resource and finish one small output:

Resource session	Minimum output
Watched a concept video	Rewrite the idea in 3 sentences
Read documentation	Run one official example
Read a blog/tutorial	Apply one idea to your course project
Read a paper or survey	Write “old problem -> new method -> project impact”

Common mistakes

• Saving many links but not running code.
• Switching resources whenever one paragraph feels hard.
• Reading theory without returning to a project.
• Trying to learn a whole framework before building the smallest demo.

Quick decision

Ask three questions before opening another resource:

1. What exact problem am I stuck on?
2. Will this resource give a different explanation, an official API answer, or a runnable example?
3. Which course task will I return to after reading it?

If you cannot answer those three, stay with the main course path.