I was very confused by this in the beginning of my journey. I was trying to learn everything involved with ML/DL, but in the end everything is already implemented with APIs, and your boss doesnt care if you know how to implement a MLP from scratch or if you use Tensorflow.
My (poor) analogy is: you don't need to know how a car works (or how to build one) in every detail to drive it. When I understood it, it was liberating.
What's not so great is the huge number of people believing to understand something when they don't, i.e. the illusion of knowledge they're getting from some of these marketing-driven courses and MOOCs. I see that in job applications. Every resume has "Deep Learning, PyTorch, Tensorflow" on it now, but if you ask them why something works (or why a variation may not work) these candidates have no idea. And for some jobs that's totally fine, but for other jobs it's not. And the problem is when you can't tell the difference.
It's kind of like putting "compilers" on your resume because you've managed to run gcc.
Interesting. Do you have an example? Is it common for people to practice ML problem-solving à la LeetCode nowadays?
I'm just making these up because the questions we previously asked were domain-specific to our applications, e.g. "why is this specific learning objective hard" or "what would you modify to help generalization in case X"
These questions are very easy to talk about for someone with a strong ML background. They may not always know the answer and often there is no right answer, but they can make reasonable guesses and have a thought process around it. Someone who just took a MOOC likely has no idea how to even approach the question.
fast.ai do stuff pretty well. FWIW, I did one of their earlier free courses and, as a maths grad, got my fill of maths learning as well as my fill of practical 'doing stuff with ML' stuff. If I didn't have my plate full I'd probably pay the 500 quid or whatever to do this course now rather than wait for the free version.
> fast.ai
> Do that and your life will change
Sounds like Emad Mostaque of Stability AI / stable diffusion thinks this course probably won't fall into "do this, no understanding needed" trap (I'm not contradicting anything you said here).
Originally took a break from being a hedge fund manager to build AI lit review systems to investigate ASD etiology for my son along with neurotransmitter pathway analysis to repurpose medication (with medical oversight) to help ameliorate his more severe symptoms.
Had 100% satisfaction from programmers with some math knowledge trying fast.ai and members of team active there, really nice take off point into a massive sector.
It digs nicely into the principles and is not a surface level course. The stable diffusion one will need some good work to get through.
But yeah my job now is to get billions of dollars into open source AI to make the world happier, happy to do my best and let the smart and diligent folk buidl.
Do you know other efforts in that direction?
Will be aggressively investing in this area and making the output available openly next year after our education launch.
If there's a way to contact you (Sharing similar challenge) I'd be happy.
I think that's somewhat of a dangerous mindset to have. If you want to create cool images you can use pre-trained models and high-level APIs without needing to understand any of the internals.
But if you want to truly understand how these models work, you need to make effort to study the basics. Maybe not predicting housing prices, but learn the foundational math and primitives behind all of the components from the ground up (and the Diffusion models are a complex beast made up of many components). And getting an intuitive understanding of how models behave when you tune certain knobs takes much longer. Many researchers in the field have spent years developing their intuition of what works and what doesn't.
Both of these are fine, but I think I think we should stop encouraging people to be in the middle. Have courses that that promise "Learn Deep Learning / Transformers / Diffusion models in 7 days!" but then go on and teach you how to call blackbox APIs, giving you an illusion of knowledge and understanding where there is none. I don't know if this applies to this specific course, but there are a bunch of those out there, and highly recommend staying away from those. I know it's a hard sell in this modern instant gratification age, but if you actually want to understand something you need to put in some possibly hard work.