psychorameses

psychorameses t1_j48la7w wrote

Reply to comment by pm_me_your_pay_slips in [D] Bitter lesson 2.0? by Tea_Pearce

For now, yeah. I'm the guy building their fancy hodgepodge theoretical linear algebra functions into efficient PyTorch backend code so it can actually do something. And the CI/CD pipelines, the serving systems and all of that. You could even say I'm contributing to the demise of those 10 engineers. Especially all the Javascript bootcamp CRUD engineers flooding NPM with god-knows-what these days.

Gotta back the winning side, not fight them. If foundation models get replaced by something else, I'll go build software for those guys and gals too.

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psychorameses t1_j47q301 wrote

Reply to [D] Bitter lesson 2.0? by Tea_Pearce

This is why I hang my hat on software engineering. You guys can fight over who has the better data or algorithms or more servers. Ultimately yall need stuff to be built, and that's where I get paid.

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psychorameses t1_j3hxgzs wrote

Reply to comment by dr-pork in [D] Best way to package Pytorch models as a standalone application by Atom_101

Best way to think of it is that it scrambles your code, but right before it runs it still needs to be unscrambled, so someone dedicated enough will be able to obtain the full source code (or at least byte code) anyway.

I hope my boss sees this because this is a very common misconception. This isn't the 90s where your only option for running code was to install it on the user's machine. You have other options. You have to assume that everything you install on a user device is no longer private. The only way to protect source code is to never distribute it in the first place, and only expose your software through web APIs.

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psychorameses t1_j2e63yf wrote

Reply to comment by Ashraf_mahdy in Incorporating Deep Learning into my MSc by Ashraf_mahdy

That distinction isn't real. In that regard, both techniques learn exactly the same thing: a best fit curve. The difference is how complicated that curve needs to be. Unless you are trying to do something specific like computer vision or natural language processing, you really don’t need DL. If you are working with simple tabular data, basic ML like linear regression will be more than enough.

In any case, the feedback for most ML projects is to start with a simple regression technique and only start complicating your models if you aren’t getting what you want. You’d be surprised to see how far a simple non-DL model gets you.

I worked in Zillow’s AI team so I know both AI and real estate analytics problems.

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