Deep-Learning-Interview-Book
A comprehensive deep learning interview preparation book covering math, CV, NLP, and recommendation systems.
Master technical interviews with a curated repository covering algorithms, math, and machine learning domains.
This repository acts as a structured reference guide for machine learning and AI engineers preparing for technical interviews. It suits job seekers targeting AI algorithm and development roles who need a comprehensive review of core concepts.
What it is
The repository organizes interview study materials into categorized documents spanning mathematics, machine learning, deep learning, computer vision, natural language processing, and recommendation systems. It includes visual mindmaps and links to supplementary job hunting notes.
Who it's for
It suits AI professionals and students preparing for rigorous technical interviews in machine learning or computer vision. It does not suit those looking for executable code libraries, production-ready APIs, or deployment frameworks.
Key features
Multi-Domain Curriculum
Covers math, machine learning, deep learning, NLP, computer vision, and SLAM in structured documents.
Recommendation Systems
Includes dedicated interview notes focused on recommendation algorithms.
Visual Mindmaps
Provides architecture and topic mindmaps for quick conceptual reviews.
Job Hunting Guides
Links directly to supplemental career advice and interview experience notes.
Up and running in 3 steps
Our honest verdict
What's great
- Aggregates essential AI interview topics into a single repository.
- Includes specialized sections for recommendation systems and SLAM.
- Provides clear visual mindmaps for high-level study planning.
- Maintains dedicated guides for programming languages like C, C++, and Python.
Where it falls short
- Lacks a stated open source license in the repository metadata.
- Content is primarily in Chinese, which may present a barrier for non-native speakers.
- Does not provide runnable code or software tools, serving purely as reading material.
Pricing & license
The repository content is freely accessible on GitHub, though the README also promotes a paid external WeChat community and knowledge base subscription priced at 149 yuan.
Open remote AI-training roles
880 open remote AI-training roles, updated November 15, 2026; most pay $20–$175 an hour. Zekai lists them free; the application goes to the employer’s platform.
Alternatives
Use it, or have us set it up
FAQ
What topics are covered in the book?
It covers math, machine learning, deep learning, reinforcement learning, computer vision, NLP, SLAM, recommendation systems, and data structures.
Is there runnable code provided?
No, the repository is a collection of study notes and documentation for interview preparation.
What language is the repository written in?
The documentation and notes are written in Chinese.
