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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?

#101

Post by simonw »

I'd spend most of that hour a day using ChatGPT, Bard and other models.

Learning how to effectively prompt an LLM is an enormous space in its own right - and there's no shortcut for it, you have to actively play with the things.

I've been using them constantly for over a year at this point and I'm still figuring out new tricks and strategies all the time.

Weirdly, knowledge of Machine Learning isn't actually that relevant to getting good at using LLMs to solve problems and build software.

Knowing how to train your own neural network will do little for your ability to build astonishingly cool software on top of existing LLMs.

Knowledge of how LLMs work is useful, because it can help you prompt them more effectively if you understand their limitations, have an idea of their training data etc.

I've seen people (who I respect) argue that deep knowledge of ML can be a disadvantage when exploring LLMs, because it can limit the way you think about and interact with them. Weird but possibly true!

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

#102

Post by orm »

Hm, not exhaustive but I think these are potentially useful to you:

The deeplearning.ai math basics for deep learning, seems self-contained. MiniTorch repo (implement your own tiny torch) seems also helpful to understand what goes on during training. MinGPT repo (to understand a basic version of GPT model structure) Dive into deep learning (textbook avail online, more focused on practical DL)

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

#103

Post by borg16 »

gwbas1c wrote: post #91

One thing to point out: Try not to let your imagination run away, or get overconfident in what AI/ML can do. I worked for a major company on an ML project for 2 years. By the time I left, I realized that: 1: The project I was working on has no improvement over ordinary statistical methods; yet the ability for people to understand the statistics (over the black box of ML) meant that the project had no tangible improve…

this answers a different question - what is the simplest possible solution to the problem at hand.

answering that requires a good understanding of the problem at hand as well as knowledge to be able to propose a simple solution that could be the starting point, and then searching for improvements over the same - assuming the improvement they bring is useful to the solution at hand.

i guess what I am trying to say is, the question asked by op and your suggestion are orthogonal imo :)

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

#104

Post by alephnan »

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 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.

Are people actually going into masters degree to learn? I thought the whole point of paying for masters is just credentialism

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

#105

Post by theusus »

RamblingCTO wrote: post #78
theusus wrote: post #49

Earlier quoted context omitted.

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.

Which book would you recommend then? Suggest only one.

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

#106

Post by PeterisP »

ekianjo wrote: post #77
camhart wrote: post #50

Earlier quoted context omitted.

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 tut…

Most of what was listed above aren't the fundamentals which would help to properly understand what's happening with neural networks but rather completely different branches of machine learning that have little in common with neural networks. If they learn SVM, naive Bayes, and gradient boosting, then their knowledge will definitely be broader but just as shallow; using your analogy, it's not like trying to program in JS without understanding for loops but rather like trying to program in JS without understanding C, COBOL and Haskell.

I'm all for learning the fundamentals properly - but those fundamentals are going to be completely things, things like core principles of statistics (limitations of correlation, confounders, bias/variance, etc), the relevant parts of calculus and linear algebra that matter for understanding optimization, the best practices for management of data, experiments and measurement to not cheat yourself, etc - not the checklist of many different, parallel methods of machine learning like decision trees or reinforcement learning, which are both useful and interesting, but not related or required to properly apply e.g. transformer-based large language models for your task.

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

#107

Post by PeterisP »

mickael-kerjean wrote: post #74
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…

> 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?

It's useful to do learning on small toy problems for ease of debugging and speed of training, so if you want to learn how to apply a powerful technique, you're pretty much inevitably going to start learning it on something for which it's absolutely ineffective overkill. E.g. a common start for learning neural nets is a task like XOR which can be solved with literally a single machine instruction instead of training a ML model for it.

But also there are many tasks for which naive Bayes works, but a NN solution can be much more accurate if you're okay with it being also much more compute-intensive. E.g. things like sentiment analysis or simple spam filters are often used as a demonstration of naive Bayes, but you can do much better with more powerful models.

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

#108

Post by RamblingCTO »

theusus wrote: post #105
RamblingCTO wrote: post #78

Earlier quoted context omitted.

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

Which book would you recommend then? Suggest only one.

Depending on the focus you're looking for I'd either say Machine Learning by Flach (really like that one!) or Artificial Intelligence by Russel & Norvig.

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

#110

Post by riku_iki »

Depending on your goal, if it happened to be hired as ML engineer, then better to focus on building resume:

1. Build small projects in the area you have passion about, examples: try to beat benchmark, classify news and track stories of your interest, build auto manga generator

2. Kaggle competitions: not sure if employers are looking at this though

3. Write blog about your journey.

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