Deep Learning 500 Questions: 500 Interview Qs
/DeepLearning-500-questions
Deep Learning 500 Questions turns deep learning theory into interview Q&A, from linear algebra through CNNs, GANs, and model compression. Reach for it if you read Chinese and want a quiz-format study guide before an AI engineer interview. Skip it if you need English content or runnable code — this is reading material only, and the book itself is marked 'to be continued'.
What is Deep Learning 500 Questions?
A Chinese-language Q&A book authored by scutan90 (Tan Jiyong) — a PhD researcher at SUSTech and Harbin Institute of Technology who also directs tech at Hanwei Smart Medical. Think of it as a large interview study guide spanning math, ML, and deep learning theory, written in interview-quiz style rather than textbook prose. The same material is sold as a printed book through Bowen Vision on JD.com.
Who is This Resource For?
Deep Learning 500 Questions is for Chinese-reading candidates prepping for AI engineer, computer vision, or machine learning interviews who want theory broken into quiz-sized entries. It works as a standalone study plan from math foundations through model optimization. Skip it if you need English content, runnable code alongside theory, or a finished, fully-proofread text — the book itself says 'to be continued'.
Comprehensive Coverage of AI Topics
- ✓Math foundations primer: vectors, matrices, derivatives, eigenvalues, and probability distributions that most interview guides take for granted.
- ✓Classical ML algorithms section: logistic regression, decision trees, SVM, Bayesian classifiers, PCA/LDA, clustering, plus ROC/AUC and confusion matrix interpretation.
- ✓Deep learning basics: forward and backward propagation, activation functions, batch normalization, weight initialization, and dropout regularization.
- ✓CNN, RNN, and GAN architecture chapters — the backbone of modern computer vision work.
- ✓Two CV application chapters on object detection and image segmentation.
- ✓Later chapters on transfer learning, hyperparameter tuning, and model compression for deployment.
Strengths
- ✓Top-down organization: math and ML basics precede deep learning and CV specifics, so you never hit a CNN backpropagation question without the underlying calculus.
- ✓Named contributors with industry credentials: scutan90/Tan Jiyong leads the project, with editors affiliated with Baidu, iQiyi, and Shunfeng Robotics.
- ✓Free to read on GitHub, even though the same material is also sold as a commercially edited print book through Bowen Vision.
- ✓Question-and-answer format mirrors how interviewers actually probe candidates — not a lecture outline, but a set of prompts you can use for self-testing.
Considerations Before Using
- △Everything — chapter headings, Q&A entries, author bios — is in Chinese. The README gives no sign of an English translation in progress.
- △GitHub tags the repo as JavaScript, which misrepresents a plain text book; don't expect runnable code or notebooks here.
- △The book's own description ends with '未完待续' ('to be continued'), so treat it as a working draft rather than finished, proofread text.
- △GPL-3.0 governs the content, but that's a software license applied to prose — the README doesn't explain how it restricts use of the book material.
Other Deep Learning Interview Resources
Frequently Asked Questions
Yes. It's sold on JD.com through Bowen Vision (博文视点) under the title 深度学习500问——AI工程师面试宝典. Lead editor is Tan Jiyong (谈继勇, scutan90 on GitHub).
The repo uses GPL-3.0, but that's a software license applied to what is fundamentally book prose. The README doesn't explain how GPL-3.0 is meant to govern the Q&A text versus executable code, so the licensing terms for the content are ambiguous.
Yes. The book has dedicated chapters on CNN, RNN, and GAN architectures, plus separate chapters on object detection and image segmentation, and later chapters on transfer learning and model compression for deployment.
The book description ends with '未完待续' ('to be continued'), and the README states no revision schedule or last-updated date. Treat the update cadence as unstated rather than active.
No. The README makes no mention of an English version — all chapter titles and Q&A content are in Chinese.
Lead editor Tan Jiyong (谈继勇, GitHub user scutan90) is a SUSTech/Harbin Institute of Technology PhD candidate and tech director at Hanwei Smart Medical. Associate editors include Guo Zizhao (Sichuan University, AI chip research), Li Jian (Tongji University, associate professor), and Dian Songyi (Sichuan University professor).
Best use cases
- •Cramming deep learning theory the week before an AI engineer interview, working section by section instead of rereading a full textbook.
- •Looking up a single concept — say, why cross-entropy replaces the quadratic cost function — without digging through a complete course.
- •Refreshing CV interview questions on CNN/RNN/GAN architectures and object detection before a computer vision loop.
- •Using the published book version (JD.com) as a structured offline study guide when you don't want to read on screen.
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