The AI Interview Syllabus: Math, SLAM, and Linux

What it does This repository is a structured index of markdown notes spanning the entire AI interview landscape: mathematics, machine learning, deep learning, reinforcement learning, computer vision, NLP, SLAM, recommendation systems, data structures, C/C++/Python, deep-learning frameworks, and even computer networks and Linux. It serves as a communal cram sheet for candidates targeting algorithm and development roles in the Chinese AI job market. The README itself is largely a table of contents linking to topical markdown files, plus a mindmap.

The interesting bit The breadth is almost comically comprehensive—most interview guides stop at transformers and CNNs, but this one keeps going through traditional image processing, SLAM, and whether you can explain TCP/IP. That scope reflects the reality of many Chinese AI engineering interviews, which often test general systems knowledge alongside model architecture trivia.

Key highlights

• Covers 15+ topic areas from math to Linux, not just model architectures.
• Includes dedicated sections for interview experience and interview tactics, not just technical facts.
• Provides a mindmap (DLIB-Mindmap.png) for visual navigation of the material.
• Content is entirely markdown-based and hosted in the repo’s docs/ folder.
• Targets both algorithm and engineering tracks (算法岗 and 开发岗).

Caveats

• The README is strictly an index; actual content depth and accuracy are only visible inside the linked markdown files.
• Prominent advertising for a paid WeChat/Zhishixingqiu job-search group dominates the bottom of the page.
• All source material is in Chinese, so non-readers will need translation tools.

Verdict Worth bookmarking if you are preparing for AI interviews in the Chinese market and want a single checklist to ensure you have not forgotten, say, Kalman filters or Python GIL semantics. Everyone else can safely skip.