Chip Huyen

@chipro

Machine learning production. Math + OSS + storytelling. Working on ML Interviews book. Prev: Nvidia, Netflix, Primer, Stanford.

Vrijeme pridruživanja: lipanj 2008.

Medijski sadržaj

  1. 10. sij

    Workera recently published a report on AI career pathways. It doesn't mention hardware. I also don't see the difference b/w SWE-ML & ML Engineer. But it highlights some important distinctions. I also like talk on the structure of AI teams

  2. 15. pro 2019.

    Some of the keywords in paper titles that have seen the most change from NeurIPS 2018 to 2019. - meta-learning, kernel methods, reinforcement learning are 🔥 - more hardware-aware, more theory-driven - recurrent & convolutional get little love Full NeurIPS recap coming soon!

  3. 13. pro 2019.

    I had a chance to discuss research w/ these amazing researchers. Takeaways: 1. Too many ML papers & most are bad. 2. For PhD apps, & look at blog & OSS too. 3. Avoid trendy topics. Work on what you believe in. 4. Research may not be applicable & it's ok.

  4. 1. stu 2019.

    When everyone is a Dr and you're just you 😅 Humbled to speak alongside these wonderful researchers and industry leaders at the annual summit. Excited about the progress my country is making in AI. If you're working on AI in Vietnam, I'd love to chat!

  5. 19. ruj 2019.

    Non-CS people: "How many lines of code do you write a day?" Me: "Um idk minus 200?" Can we take a moment to appreciate all the relentless deleters who are willing to dig into the mess that is our code and make it concise and readable?

  6. 6. ruj 2019.

    The numbers of accepted papers from universities are more evenly distributed. Stanford, CMU, and MIT surprisingly overtook UC Berkeley by a large margin. Non-US institutions such as Oxford, Tsinghua, INRIA, and Peking University are rapidly catching up.

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  7. 6. ruj 2019.

    Stuck at the airport so I browsed papers. Out of 1429 accepted papers, 167 (~12%) have at least one author from Google/DeepMind, same as Microsoft, Facebook, IBM, & Amazon combined. Is there any stats on the % of reviewers who are Google/DeepMind affiliated?

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  8. 26. kol 2019.

    Okay GPT-2 I made him eat a sandwich now what (if you haven't tried it out already GPT-2 gives fabulous dating advice )

  9. 21. kol 2019.

    Companies with the hardest interviews (as perceived by candidates) are Google, Airbnb, and Amazon.

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  10. 21. kol 2019.

    Senior candidates are harder to please than junior candidates. This might explain the abysmal Netflix interview experience. While all other companies keep their shares of senior interviews to under one third, Netflix exclusively hire senior positions.

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  11. 21. kol 2019.

    The more negative experience a candidate has, the less likely they are to accept the offer. If a candidate who receives an offer has a positive experience, they accept the offer with probability 87%. If that candidate has a negative interview experience, the yield rate is 1/3.

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  12. 21. kol 2019.

    Candidates with offers are more likely to have a positive experience (correlation 0.75). Companies that give the best candidate experiences are Salesforce, Intel, and Adobe.

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  13. 21. kol 2019.

    Everyone complains that the interview process is broken. It’s not entirely true, at least from the perspective of the candidates who get interviews. 60% of candidates report a positive interview experience.

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  14. 21. kol 2019.

    Referrals matter, a lot. For junior roles, about 10 - 20% of candidates that get to onsites are referred, with Uber leading the chart with almost 30%. For senior roles, that numbers are higher. Salesforce, Uber, and Cisco all have ~30% of their senior onsite candidates referred.

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  15. 21. kol 2019.

    Strong correlation bw onsite-to-offer rate and offer yield rate (% of candidates who accept their offers). The more selective the company is, the less likely a candidate is to accept their offer. Candidates that pass interviews at FAANG are likely to have other attractive offers.

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  16. 21. kol 2019.

    Each review consists of: - result (no offer/accept offer/decline offer) - difficulty (easy/medium/hard) - experience (positive/neutral/negative) - review (application/process/questions) The largest SWE employers are Google, Amazon, Facebook, and Microsoft.

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  17. 9. kol 2019.

    You know your friend works at OpenAI when his phone autocorrects "ago" to "agi" 😆

  18. 3. kol 2019.

    8. Introduction to Reinforcement Learning by DeepMind RL is hard, but David Silver is here to the rescue. This course provides a great introduction to RL with intuitive explanations and fun examples, taught by one of the world’s leading experts.

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  19. 2. kol 2019.
    Odgovor korisniku/ci

    Okay people it's "banh mi" as in the Vietnamese food not "bahn mi" as in the autobahn that takes you away from unhappy food.

  20. 27. srp 2019.

    What's the worst/best email you've received from a recruiter? Mine.

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