An

Answers to Chip Huyen's ML Interview Questions

Hacker News

Answers to Chip Huyen's ML Interview Questions

Hi HN, When I was preparing for ML interviews in my first job hunt, I came across Chip Huyen's ML interview questions [1] which I found incredibly helpful. Over several weeks I compiled my answers into a LaTeX document, which I have since open sourced. I thought this document would be useful to other people preparing for their ML roles, especially because there is no centralized and comprehensive repository for most of the answers. Best, Zafir [1] https://huyenchip.com/ml-interviews-book/

Share card

Actual performance

3points
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Indie HackersFits the IH revenue-focused audience · Strong signals: latex · Missing: supports, reddit linkedin, podcasting
65%65% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Hacker NewsStrong engagement from HN community · Strong signals: open source, io · Missing: https docs, excited, just released
58%58% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: answers · Missing: mobile apps, ios, personal
53%53% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: open · Missing: mac, agents, macos
25%25% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
7%7% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

Similar products

ML
ML Q&A – Get answers to questions about ML frameworks62%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

ML Q&A – Get answers to questions about ML frameworks

Hacker News2
ML
ML Questions Answered53%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

ML Questions Answered

Hacker News1
Pe
Personally tailored interview questions and answers37%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Personally tailored interview questions and answers

Hacker News2
To
Top Solidity Interview Questions37%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Top Solidity Interview Questions

Hacker News1
Ra
Rails Interview Questions36%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Rails Interview Questions

Hacker News1
Br
Brren – Answers to Any Questions60%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Brren – Answers to Any Questions

Hacker News1
Deli Dahi - Bilgi Yarışması
Deli Dahi - Bilgi Yarışması79%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Crazy Questions. Brilliant Answers.

Indie Hackers1games
Interview questions
Interview questions24%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

the top technical interview question and answers.

Indie Hackers
Tr
Translating CHIP-8 binaries to Posix C37%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Translating CHIP-8 binaries to Posix C

Hacker News8
Fo
Fortran Chip-8 Interpreter46%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Fortran Chip-8 Interpreter

Hacker News5