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

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

#71

Post by tgittos »

This is exactly the boat I'm in. I have a 1hr train commute to work that I spend skilling up in AI. I've been following the space for about 15 years and have done a bunch of self learning of earlier ML techniques (the early Stanford ML MooCs) so I'm not coming in cold. What I'm doing is:

- Following along with Karpathy's videos, which has been mentioned: https://karpathy.ai/zero-to-hero.html

- About to follow along with CS 231n, also mentioned: https://www.youtube.com/watch?v=NfnWJUyUJYU&list=PLkt2uSq6rB...

- Trying ideas and theories in a Jupyter notebook

- Reading papers

I would agree with other commenters that recommend learning how to implement a paper. As someone who barely managed to get their undergraduate degree, papers are intimidating. I don't know half the terms and the equations, while short, look complex. Often it will take me several reads to understand the gist, and I've yet to successfully implement a paper by myself without checking other sources. But I also know that this is where the tech is ultimately coming from and that any hope of staying current outside of academia is dependent on how well I can follow papers.

I've been doing this for about a month now, and I feel I definitely understand more of the theory of how most of this stuff works and can train a simple attention based model on a small-ish amount of data. I don't feel I could charge someone money for my skills yet, but I do feel that I will feel ready with about 6 months - 1 year of doing this.

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

#72

Post by muditsrivastava »

I built aiplanet.com where numerous beginners accessed free AI learning resources provided by a diverse group of contributors (primarily experienced AI practitioners). Building on the common advice, here are some insights to consider, given your existing context:

- AI/ML is diverse, with data scientists specializing in different areas. I know AI experts who have still not delved into LLMs; they have their specific focus areas. AI/ML skills encompass a wide range of topics, and data scientists often have specific focus areas. Continuous exploration and reading are crucial. Resources like paperswithcode.com are valuable for discovering new research areas and domains.

- While time-consuming, Kaggle offers exposure to robust modeling and validation skills. These skills are critical, though they are only a fraction of what's needed for real-world projects. It's beneficial to expand beyond these skills. This being said, it does give bragging rights. I've seen company founders, like those at H20.ai, often highlight their Kaggle Grandmasters.

- My current role is at Pathway.com. Over 80% hold of my colleagues PhDs, and our CTO has co-authored with folks like Geoff Hinton and Yoshua Bengio (I find that cool actually :)). But this environment may reflect my bias towards academic research. This being I said, I believe that strong foundational understanding is essential and also valued, especially when tackling complex challenges.

- Active participation in forums and communities related to the frameworks you use is highly recommended, like TensorFlow User Groups. At Pathway.com, we welcome those interested in stream data processing to our community. Engaging in these forums offers the chance to receive support from the original creators and leading community members. Other notable communities include DataTalks.Club and MLOps.Community.

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

#74

Post by mickael-kerjean »

DeathArrow wrote: post #21
janalsncm wrote: post #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 g…

ML is so much more than just neural networks. I would start by taking a free university level course in statistics. Then I would continue with the basics: SVM, linear regression, naive Bayes, gradient boosting, neural nets etc. I would not only train and fine tune them, but I would also build simple ones myself instead of just using libraries. Then I would continue to what you said, participate in Kaggle competitions…

> SVM, linear regression, naive Bayes

When I studied ML in 2012, the very first course started with naive Bayes and went one from there. A decade after being away, I see a lot of people around me starting with neural nets to train a model that naive Bayes would be plenty enough for and never heard about naive Bayes. Is that only my experience?

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

#75

Post by ekianjo »

wanderingmind wrote: post #32
DeathArrow wrote: post #21

Earlier quoted context omitted.

ML is so much more than just neural networks. I would start by taking a free university level course in statistics. Then I would continue with the basics: SVM, linear regression, naive Bayes, gradient boosting, neural nets etc. I would not only train and fine tune them, but I would also build simple ones myself instead of just using libraries. Then I would continue to what you said, participate in Kaggle competitions…

This type of bottom up approach is terrible idea for a fast moving area like ML. Ultimately to get a job and make money in the area, you need to solve customer problems, starting with libraries and fine tuning is what needs to happen first. However you should try to learn fundamentals as you go and when you get stuck. It will take a very long time if you have a full time job or a student studying something else and d…

> his type of bottom up approach is terrible idea for a fast moving area like ML.

Of course not. When you learn this way you will realize there are a myriad of problems that can be solved with more simple algorithms. Trying to make every problem fit to neural networks is pure cargo culting.

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

#76

Post by mark_l_watson »

I think you have listed 8 strategies that are both good, and you ordered them in most important strategies first.

For courses, Andrew Ng’s classes have always been good, starting with his Stanford ML class, Coursera deep learning classes, and now his short mini-classes on being an effective LLM practitioner.

Textbooks on LLMs are likely to quickly be out of date, at least I struggle to keep my LangChain/LlamaIndex book current.

My advice to you is to try to get into a paid AI job as your highest priority, and that is a lot of work: identifying possible employers, preparing for interviews, and having persistence. Some of the interesting AI work you might find will not be with “tech” companies, but rather small or medium profitable businesses that need to use ML, DL, LLMs lightly - just a small part of their successful businesses.

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

#77

Post by ekianjo »

camhart wrote: post #50
tsurba wrote: post #39

Earlier quoted context omitted.

Getting a job to make money and solve customer problems as fast as possible was not a stated goal by the OP. Besides, you are also wrong, having good fundamentals in the maths will help you pick up new methods much faster as they pop up. And especially if you want to come up with new methods (as in research), there are no shortcuts.

They made a reasonable assumption. Abstraction helps you to be productive. It's certainly good to understand everything all the way down to the actual physics behind computing, but its not necessary especially at the start. I don't have to know exactly how logic gates work to program in JS and make a difference. I assume the same applies to ML. Motivation is what prevents most people from learning difficult to learn…

> I don't have to know exactly how logic gates work to program in JS and make a difference.

Thats a stupid analogy to the above discussion. A better analogy would be trying to program in JS without understanding for loops and basic programmation concepts.

Thats typically what I observe with younger folks jumping into neural nets directly. They have a very shallow understanding of anything and survive with youtube tutorials.

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

#78

Post by RamblingCTO »

theusus wrote: post #49
RamblingCTO wrote: post #25

Earlier quoted context omitted.

That's only deep learning. There's so much more in machine learning and I think getting the basics right is more important than focusing only on one area.

It does claim to teach from beginning. Once comfortable they can try to cover other domains toi.

I had a look at the index and it does not cover nearly enough to have a solid foundation of ml.

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

#79

Post by mortallywounded »

janalsncm wrote: post #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 g…

> 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.

I have a masters degree in computer science and took a fair share of ML graduate courses. That pretty much summed up what I was thinking. They basically forced me to sit and learn something I wouldn't have alone.

Now-- I'm not saying you need to go to grad school. You could buy some ML textbooks and force yourself through them and go from there... but how many people have that grit? I wouldn't have been one of them :)

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

#80

Post by nkzd »

hereonout2 wrote: post #45

Presuming you want to work in the field and already have software development experience why not look at the confluence between ML and engineering? Things like ML ops, application of DevOps, testing and ci/cd in the ml space, how to train across multiple gpus, how to actually host an LLM especially at scale and affordably. In my experience there are hundreds of candidates coming from academia with strong academic bac…

Hey, I am a classic backend software engineer looking to learn how to do things you mentioned. I believe if I learn these skills, I will know how to make "shovels" during gold rush :)

Can you recommend any learning resources for things you mentioned? I don't have an option to learn these on my current job, so it will be hard to structure CV to prove my future employers I know them when I don't have real world experience.

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