What is deep-learning-interview-book?

amusi/deep-learning-interview-book — explained in plain English

Analysis updated 2026-06-24

★ 8,832Audience · researcherComplexity · 1/5Setup · easy

Mindmap

mindmap
  root((DL Interview Book))
    Topics
      Mathematics
      Machine learning
      Deep learning
      Computer vision
    More topics
      NLP
      Reinforcement learning
      Algorithms
    Format
      Markdown documents
      Mind map image
    Audience
      Chinese AI candidates
      Job seekers
    Access
      Free GitHub reading
      Paid WeChat group

mindmap root((DL Interview Book)) Topics Mathematics Machine learning Deep learning Computer vision More topics NLP Reinforcement learning Algorithms Format Markdown documents Mind map image Audience Chinese AI candidates Job seekers Access Free GitHub reading Paid WeChat group

Click or tap to explore — scroll the page freely

What do people build with it?

USE CASE 1

Review key machine learning and deep learning topics before a technical AI job interview.

USE CASE 2

Study algorithm and data structure questions commonly asked at tech companies hiring AI engineers.

USE CASE 3

Use the included mind map to identify which topic areas to prioritize given limited preparation time.

What is it built with?

Markdown

How does it compare?

	amusi/deep-learning-interview-book	garrettj403/scienceplots	melkeydev/go-blueprint
Stars	8,832	8,832	8,832
Language	—	Python	Go
Setup difficulty	easy	moderate	easy
Complexity	1/5	2/5	2/5
Audience	researcher	researcher	developer

Figures from each repo's GitHub metadata at analysis time.

How do you get it running?

Difficulty · easy Time to first run · 5min

So what is it?

This repository is a Chinese-language study guide aimed at people preparing for technical job interviews in AI and machine learning. The title translates to "Deep Learning Interview Bible." It is a collection of documents organized by topic to help candidates review the knowledge areas most commonly tested in interviews at tech companies hiring for AI roles. The guide is divided into sections covering mathematics, machine learning, deep learning, reinforcement learning, computer vision, traditional image processing, natural language processing, SLAM (a robotics and mapping topic), recommendation algorithms, data structures and algorithms, programming languages including C/C++ and Python, and popular deep learning frameworks. There is also a section on interview experience and interview tips. The repository appears to be primarily a reading resource rather than runnable code. Most of the content lives in Markdown documents linked from the main index. There is also a mind map image showing the overall structure of the material. The README includes a promotion for a paid WeChat group for AI job seekers, priced at 149 yuan per year, where members can ask questions and share resources. This paid community aspect is separate from the free GitHub content. All content is written in Chinese, so the primary audience is Chinese-speaking students and professionals preparing for AI-related technical roles.

Copy-paste prompts

Prompt 1

Quiz me on the most commonly asked deep learning interview questions covering backpropagation, loss functions, and optimization, then explain each answer clearly.

Prompt 2

Explain the difference between supervised learning, unsupervised learning, and reinforcement learning as if preparing for a technical interview at a Chinese AI company.

Prompt 3

Give me a 4-week study plan covering math, ML, deep learning, and NLP for an AI engineering interview at a top tech firm.

Prompt 4

List the 10 most important computer vision concepts I must understand before an AI engineering interview, with one-sentence explanations.

Frequently asked questions