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Prikvačeni tweet
I wrote an 8k word doc on machine learning systems design. This covers: 1. Project setup 2. Data pipeline 3. Training & debugging 4. Serving with case studies, resources, and 27 exercises. This is the 1st draft so feedback is much needed. Thank you!https://github.com/chiphuyen/machine-learning-systems-design/blob/master/build/build1/consolidated.pdf …
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I have this working theory that more vague the description of your AI startup, the higher your valuation. What does "liberate your data", "allow data to move like the air you breathe”, “we lived and breathed data science”, “AI that delivers impact, not accuracy” even mean?
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2010: mobile first 2016: AI first 2020: privacy first It’s exciting to see so many startups working on data privacy. My prediction is that 5 years from now, we'll look back and cringe at how primitive we are all about our privacy right now.
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10 hours fixing a bug = 5h looking for that bug + 1m fixing + 4h59m complaining to everyone about it
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I talk to a lot of ML tooling companies (3 startups today!) and I've spent so much time building infra for ML production. I'm thinking of starting a monthly newsletter about ML production ecosystem but I'm not sure if the world needs another newsletter. What do you think?
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I told my mom I wish someone would invite me to give a talk in Australia so I could visit. The week after I got the invite from University of Adelaide


I also really want to visit Brazil, Japan, South Africa. Can someone invite me too? Is it possible to be lucky twice
https://twitter.com/ecms_uofa/status/1220198234804670465 …
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I analyzed compensation & level details of 19k tech workers to find answers to: 1. How long does it take for SWEs to reach a certain level? 2. Compensations across jobs/levels? 3. Do women get paid less than men in tech? 4. Is there a deadline for SWEs?https://huyenchip.com/2020/01/18/tech-workers-19k-compensation-details.html …
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My date: “You’re my number 1.” Me: “Are you zero indexed or one indexed?” Me: *single*
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- 2.7 million have enrolled in Andrew Ng’s Machine Learning course - Geoffrey Hinton has been cited 340k times - TensorFlow has been used in 60k OSS projects Hypothesis: in 5 years, when these millions of students have gained hands-on experience, we'll have AI skills overflow.
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I've been at this startup for less than a month and I've been exposed to so many problems I didn't even know existed. I'm of the increasing belief that everyone should try a startup early in their career, e.g. within the first 3 years/before settling into complacency.
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Ayy how's your Saturday night going I just spent the last 3 hours trying to find that extra comma in a 20k row csv file and I'm beginning to question my entire career choice
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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
@josh_tobin_ talk on the structure of AI teams https://www.youtube.com/watch?v=Qb3RhwNb4EM …pic.twitter.com/KysDChOjgR
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I don't understand people who don't need coffee it's like going through life without cheat codes
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What are some of the best-written papers you've come across? My favorite so far: Computing Machinery and Intelligence by Alan Turing. A great example of complex ideas made easy to grasp: Turing explained intelligence using party games!
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A reminder for those hoping to get a machine learning role at a startup in 2020: most startups aren't looking for ml experts. They want good engineers, sales people, developer advocates. But don't let that deter you. Hard work pays off, though not always in the form you expected.
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These books shaped my last decade, and since it was my coming-of-age, these books shaped who I am. I've coerced some of my friends to show me their lists and loved everything I saw. I would love to see your list too :-)https://huyenchip.com/2019/12/28/books-that-shaped-my-decade.html …
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Reasons I love writing and think everyone should write more: 1. To organize your thoughts 2. To learn: the best way to learn is to write/teach 3. To keep you accountable 4. To document your progress 5. To write better 6. To put your name out there 7. To enjoy the beauty of words
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Looking back, my last decade was like a neural network. Some parts were linear. Some were nonlinear. I never seemed to get enough data, and always got stuck in local minima. There was a lot of learning. I can't explain how any of it worked, but the results came out alright.
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It's incredibly sad for me to say that my time at NVIDIA has ended. I'm grateful for the chance to work w/ so many wonderful people on challenging projects. As I'm going on a new adventure, I put down a quick note on the lessons I learned over the year.https://huyenchip.com/2019/12/23/leaving-nvidia-lessons.html …
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When you get a new notification and hope it's your crush but it's just another recruiter asking if you're interested in joining a machine learning startup that doesn't have a client yet but already has a 9-figure valuation
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8 years ago today, I lost my grandpa who raised me. They told me he died saying my name. He wanted to see me one last time, and I couldn't make it. If you love someone, please make time to see them. I'd give the world to see my grandpa again, but even the world isn't enough.
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