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> If I want to learn how to create cool images, I want to spend as little time as possible predicting housing prices in the Bay Area. I think that's somewhat o

> If I want to learn how to create cool images, I want to spend as little time as possible predicting housing prices in the Bay Area. 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.

jan_Inkepa 4y ago
> 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. 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.
boredemployee 4y ago
>> But if you want to truly understand how these models work, you need to make effort to study the basics. 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.
- mudrockbestgirl 4y ago
  I agree, and I think that's the first use case I mentioned above. You can use Deep Learning libraries and pre-made models without a deep understanding of anything and you get some nice results. And that's great. 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.
  - neodypsis 4y ago
    > but if you ask them why something works (or why a variation may not work) Interesting. Do you have an example? Is it common for people to practice ML problem-solving à la LeetCode nowadays?
    - mudrockbestgirl 4y ago
      It's the opposite of leetcode because it tests understanding, not memorization. For example, you could ask why position embeddings are necessary and what would happen without, reasoning behind certain terms in an objective function and what would happen without them, which part of an architecture the bottleneck for convergence is, intuitively what tuning a certain hyperparameter does, show them a timeseries of gradients for something that doesn't convergence and ask what's wrong with them, etc. 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.
thingification 4y ago
https://nitter.namazso.eu/jeremyphoward/status/1568843940690030592#m https://nitter.namazso.eu/jeremyphoward/status/1568843940690... > 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).
- throwaway287391 4y ago
  AFAIK Emad Mostaque is not (yet) an AI expert at all, he's a rich guy (former hedge fund manager) building a business that provides the funding for AI experts to do their thing. Stable diffusion itself was built by a team of academics [1], Emad is not a coauthor. Not to take away anything from what he's accomplished -- it's quite incredible -- but it doesn't mean he knows how to (learn to) build AI systems or do AI research himself. [1] https://github.com/CompVis/stable-diffusion https://github.com/CompVis/stable-diffusion
  - emadm 4y ago
    I am not a coauthor but do know a decent amount about AI systems from a maths and computer science degree from Oxford, couple decades coding and being lead architect on https://hai.stanford.edu/watch-caiac https://hai.stanford.edu/watch-caiac amongst other stuff :) 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.
    - Royi 4y ago
      That's interesting. How far did you get utilizing AI for treating ASD? Do you know other efforts in that direction?

jan_Inkepa 4y ago

> 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. 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.

boredemployee 4y ago

>> But if you want to truly understand how these models work, you need to make effort to study the basics. 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.

mudrockbestgirl 4y ago
I agree, and I think that's the first use case I mentioned above. You can use Deep Learning libraries and pre-made models without a deep understanding of anything and you get some nice results. And that's great. 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.
- neodypsis 4y ago
  > but if you ask them why something works (or why a variation may not work) Interesting. Do you have an example? Is it common for people to practice ML problem-solving à la LeetCode nowadays?
  - mudrockbestgirl 4y ago
    It's the opposite of leetcode because it tests understanding, not memorization. For example, you could ask why position embeddings are necessary and what would happen without, reasoning behind certain terms in an objective function and what would happen without them, which part of an architecture the bottleneck for convergence is, intuitively what tuning a certain hyperparameter does, show them a timeseries of gradients for something that doesn't convergence and ask what's wrong with them, etc. 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.

thingification 4y ago

https://nitter.namazso.eu/jeremyphoward/status/1568843940690030592#m https://nitter.namazso.eu/jeremyphoward/status/1568843940690... > 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).

throwaway287391 4y ago
AFAIK Emad Mostaque is not (yet) an AI expert at all, he's a rich guy (former hedge fund manager) building a business that provides the funding for AI experts to do their thing. Stable diffusion itself was built by a team of academics [1], Emad is not a coauthor. Not to take away anything from what he's accomplished -- it's quite incredible -- but it doesn't mean he knows how to (learn to) build AI systems or do AI research himself. [1] https://github.com/CompVis/stable-diffusion https://github.com/CompVis/stable-diffusion
- emadm 4y ago
  I am not a coauthor but do know a decent amount about AI systems from a maths and computer science degree from Oxford, couple decades coding and being lead architect on https://hai.stanford.edu/watch-caiac https://hai.stanford.edu/watch-caiac amongst other stuff :) 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.
  - Royi 4y ago
    That's interesting. How far did you get utilizing AI for treating ASD? Do you know other efforts in that direction?