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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?

#111

Post by markha »

atomicnature wrote: post #26
duckworthd wrote: post #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 f…

Yes, I was leaning more towards the "personal project" idea as well, something around document understanding. I subscribe to the "learning by doing/immersion" philosophy as well (upto a large extent). The problem with projects is one's understanding tends to go more and more specialised, and collaborating/connecting with other ML engineers requires a broader knowledge base sometimes. Also, for giving advice and usefu…

Greg Brockman's blog[1] has few links on how he picked up ML. Another link at [2] describes the path Michal(blog's author) followed (though it's aligned to "how i got into ..."). Both these blogs walk through how they were able to get into the ML bits of things. They have bunch of links (ex: [3]).

I think it'll help if you can get a job at a company who's main focus is ML, you'll talk to folks who are doing research or solving problems using ML, you'll learn. If not, i hope these links help as folks there (people way smarter than me, a swe) had similar question and documented the steps they took to reduce the gaps in their understanding.

[1] - https://blog.gregbrockman.com/how-i-became-a-machine-learnin... [2] - https://agentydragon.com/posts/2023-01-11-how-i-got-to-opena... [3] - https://github.com/jacobhilton/deep_learning_curriculum

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

#113

Post by rramadass »

1. Get An Introduction to Statistical Learning with Applications in R/Python (aka ISLR/ISLP) by Hastie et al. Read this from cover-to-cover and make sure that you understand the concepts/ideas/nuances/subtleties explained.

2. Keep a couple of Mathematics/Statistics books handy while you are going through the above. When the above book talks about some Maths technique you don't know/understand you should immediately consult these books (and/or watch some short Youtube videos) to grasp the concept and usage. This way you learn/understand the necessary Mathematics inline without being overwhelmed.

This is the simplest and most direct route to studying and understanding AI/ML. Everything else mentioned in this thread should only come after this.

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

#116

Post by janalsncm »

DeathArrow wrote: post #54
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. I don't regard time spent in University to learn how logical gates work and many other useful things, as a loss of time. And, as a web developer/architect (after many other industries I worked in), I typically make more money than peers who don't. And knowing how things work, helped me immensely in my career. Not everything is…

> I don't regard time spent in University to learn how logical gates work and many other useful things, as a loss of time.

It’s unquestionably a loss of time, the only question is whether it’s an optimal use of time. This depends on your goals. For most software engineers looking to use ML, starting with existing frameworks and knowledge and drilling down as necessary is the most prudent method.

We all have to make sacrifices in what we learn. Even for yourself, the topics you chose to learn about implicitly left out other parallel topics you didn’t learn about. And you also didn’t learn everything from the least abstracted, most fundamental level. We need to choose the appropriate level of abstraction for the problem at hand, which will depend on each person’s goals.

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

#117

Post by janalsncm »

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…

There’s a reason people start with YouTube tutorials (which, let’s face it, are responsible for many of us passing undergrad CS classes). They give a broad, approachable explanation of the topic.

> without understanding for loops and basic programming concepts

Depending on what you mean by “understand” I would guess most software engineers don’t “understand” for loops either. For loops are an abstraction built on the CPU instruction set provided by each programming language. We use them with the knowledge of how they behave, which is the correct level of abstraction nearly 100% of the time.

And in CS undergrads, we don’t throw people into a course on assembly first. First they learn something like Java or Python, and only later dig deeper into more fundamental concepts. That’s not an accident.

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

#118

Post by janalsncm »

IKantRead wrote: post #97
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…

> don’t waste your time writing your own neural net and backprop. I don't think you should be combining writing a neural network with doing backprop since I don't know anyone working with serious ML who is not using some sort of automatic differentiation library to handling the backprop part for them. I'm not entirely sure people even know what they're saying when they talk about backprop these days, and I suspect th…

> the optimization part of the learning is really the heart of modern machine learning

It’s an important component but I wouldn’t say it’s the main factor. ML is ultimately about your data, so understanding it is critical. Feature selection and engineering, sampling, subspace optimization (e.g. ESMMs) and interpreting the results correctly are really the main places you can squeeze the most juice out. Optimizing the function is the very last step.

Basically, you can go ahead and optimize down to the very bottom of the global min but a model with better features and better feature interactions is going to win.

Further, there are a ton of different optimizers available. SGD, Adam, Adagrad, RMSProp, FTRL, etc. With just one hour a day, you could spend six months simply writing and understanding the most popular ones.

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

#119

Post by janalsncm »

mortallywounded wrote: post #79
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…

I hate to say it, but the diploma also matters. Having an MS next to your name means employers will give you the time of day that others won’t get. I really don’t like that this is the way things work, but it is.

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

#120

Post by janalsncm »

alephnan wrote: post #104
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

Technically you could do it on your own. Practically speaking? I quit my job and went to school full time. I spent 2 years studying the stuff practically 10 hours a day. The only way this is socially acceptable is if you get a piece of paper at the end which says you did it.
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