Live data from Hacker News

Ask HN: Daily practices for building AI/ML skills?

news.ycombinator.com

11–20 of 135 posts

Re: Ask HN: Daily practices for building AI/ML skills?

#11
post #5
post #2

[flagged]

What is the economic moat for a skill that any software engineer can learn with an hour a day investment?

The moat is you'll be at a level higher than the person who doesn't put in an hour a day.

Also, if it puts you at a few levels higher, and you keep learning more, combined with an already strong knowledge base... eventually combined experiences lead to something quite valuable and useful.

Re: Ask HN: Daily practices for building AI/ML skills?

#12
post #5

Earlier quoted context omitted.

What is the economic moat for a skill that any software engineer can learn with an hour a day investment?

The moat is you'll be at a level higher than the person who doesn't put in an hour a day. Also, if it puts you at a few levels higher, and you keep learning more, combined with an already strong knowledge base... eventually combined experiences lead to something quite valuable and useful.

So it's like the new react

Re: Ask HN: Daily practices for building AI/ML skills?

#13
post #7
post #2

[flagged]

Nice how 1 hour turned into 70 minutes. From personal experience: Learning and understanding the fundamentals via courses and personal projects/implementation is different from keeping up to date with blogs, newsletters etc. I suggest dedicating the full hour to 1 thing, but alternating between courses (i.e coursera), reading (i.e the batch) and building/coding.

LLMs can't do math but if someone pays me $80B then I can teach any LLM how to do arithmetic by first building an AGI.

Re: Ask HN: Daily practices for building AI/ML skills?

#14
What's worked well for me: Find a way to put what AI/ML on your critical path. Think of it like learning a new language: classes, lessons, and watching TV helps, but nothing works like full-on immersion. In the context of AI/ML, that means find a way to turn AI/ML into your full-time job or school. It's not easy! But if you do, you'll see endless returns.

If you don't have a solid enough footing to get a job in the field yet, the next best thing in my opinion: find a passion project and keep cooking up new ways to tackle it. On the way to solving your problem, you'll undoubtedly begin absorbing the tools of the trade.

Lastly, consider going back to school (a Bachelor's or Master's, perhaps?). It'll take far more than 1 hour/day, but I promise you, you'll see results far faster and far more concretely than any other learning strategy.

Good luck!

Context: I've been a Researcher/Engineer at Google DeepMind (formerly Google Brain) for the last ~7 years. I studied AI/ML in my BS and MS, but burnt out of a PhD before publishing my first paper. Now I do AI/ML research as a day job.

Re: Ask HN: Daily practices for building AI/ML skills?

#15
post #7
post #2

[flagged]

Nice how 1 hour turned into 70 minutes. From personal experience: Learning and understanding the fundamentals via courses and personal projects/implementation is different from keeping up to date with blogs, newsletters etc. I suggest dedicating the full hour to 1 thing, but alternating between courses (i.e coursera), reading (i.e the batch) and building/coding.

Easy. Stay off of reddit and you saved 10 minutes.

Re: Ask HN: Daily practices for building AI/ML skills?

#18
I got a masters degree in ML at a good school. I will say there’s pretty much nothing they taught me that I couldn’t have learned myself. That said, school focused my attention in ways I wouldn’t have alone, and provided pressure to keep going.

The single thing which I learned the most from was implementing a paper. Lectures and textbooks to me are just words. I understand them in the abstract but learning by doing gets you far deeper knowledge.

Others might suggest a more varied curriculum but to me nothing beats a one hour chunk of uninterrupted problem solving.

Here are a few suggested projects.

Train a baby neural network to learn a simple function like ax^2 + bx + c.

MNIST digits classifier. Basically the “hello world” of ML at this point.

Fine tune GPT2 on a specialized corpus like Shakespeare.

Train a Siamese neural network with triplet loss to measure visual similarity to find out which celeb you’re most similar to.

My $0.02: don’t waste your time writing your own neural net and backprop. It’s a biased opinion but this would be like implementing your own HashMap function. No company will ask you to do this. Instead, learn how to use profiling and debugging tools like tensorboard and the tf profiler.

Re: Ask HN: Daily practices for building AI/ML skills?

#19

I got a masters degree in ML at a good school. I will say there’s pretty much nothing they taught me that I couldn’t have learned myself. That said, school focused my attention in ways I wouldn’t have alone, and provided pressure to keep going. The single thing which I learned the most from was implementing a paper. Lectures and textbooks to me are just words. I understand them in the abstract but learning by doing g…

This seems like great advice.

You say don't write your own neural net and backprop implementation. That makes sense to me. What do you suggest using instead, for your suggested projects? I'm guessing tensorflow, based on your suggestions on profiling and debugging tools? Do the papers / projects you suggest map straightforwardly onto a tensorflow implementation, rather than a custom one?

Re: Ask HN: Daily practices for building AI/ML skills?

#20

I got a masters degree in ML at a good school. I will say there’s pretty much nothing they taught me that I couldn’t have learned myself. That said, school focused my attention in ways I wouldn’t have alone, and provided pressure to keep going. The single thing which I learned the most from was implementing a paper. Lectures and textbooks to me are just words. I understand them in the abstract but learning by doing g…

Love the suggestions, especially the one about implementing papers.

Do you have any starters on how one selects papers in the early days, to implement?

Also - any great papers you recommend beginners expose themselves to?

Post reply on HN