(Replying to PARENT post)

Assuming you have the math and algorithmic background, I would start by reading the “attention is all you need” paper. After reading, attempt to build a baby transformer model in PyTorch. After that, consider constructing some of the building blocks without libraries to understand how they work.
👤nikhizzle🕑2y🔼0🗨️0

(Replying to PARENT post)

I read this exact advice often here on HN and I can’t help but wonder.

Is the person writing it just repeating something they read? Is it just because they like the ´coding from first principles’ aesthetics?

I mean let’s imagine that someone does read that paper, and manage to replicate the code (quite an effort from someone coming from outside AI and academia).

Then what? I doubt it’s particularly illuminating. That doesn’t really qualify for a job by itself. So what’s the goal there? Is it just a thing to say to look like a cool hacker that code from scratch?

👤hcks🕑2y🔼0🗨️0

(Replying to PARENT post)

Just a quick reminder to everyone reading that AI / ML (let's face it, it's 0.1% AI and 99.9% ML) is still a ton more than just SOTA Deep Learning models. Depending on where you work, it could be all classical machine learning methods, and zero deep learning - or the other way around.

Having a broad enough understanding in ML would be a good starting point, along with solid SW engineering skills.

👤TrackerFF🕑2y🔼0🗨️0

(Replying to PARENT post)

And would you consider this resource to contain "the math and algorithmic background" necessary? Or is it overkill/missing some things?

https://www.freecodecamp.org/news/all-the-math-you-need-in-a...

👤dragonmouse🕑2y🔼0🗨️0

(Replying to PARENT post)

I worry that even if self-taught, I wouldn't have the credentials (job experience or degree), to do a full-time ML job.

Our my concerns unfounded?

👤itake🕑2y🔼0🗨️0