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This is exactly the kind of course I’ve wanted to do for some time now. Even before stable diffusion it felt like other media synthesis applications like StyleG

This is exactly the kind of course I’ve wanted to do for some time now. Even before stable diffusion it felt like other media synthesis applications like StyleGAN were what I wanted to learn, but most machine learning courses focus on more traditional data science topics. Of course you can start with a more traditional course and then learn something like stable diffusion afterwards, but as a newbie it’s quite hard to figure out where to even start. A full-fledged course that takes you exactly where you want to go is a lot easier and I think it can help learners to stay motivated because they have a clear goal in mind. 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.

mudrockbestgirl 4y ago
> 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?
elephanlemon 4y ago
It’s a bit dated now, but the DeepLearning.ai GANs specialization covers topics through StyleGAN. If you have no experience with ML at all I would probably start with the first two courses of their Deep Learning specialization and then jump into the GANs specialization. I would also highly recommend FastAI’s Deep Learning for Coders (and their new course that came out this year). You’ll start immediately with some cool applications (basic image recognition and NLP) and then drill down from there to learn how they work in detail. It’s set up such that you can learn as much as you want (basics with no depth: first chapter; basic understanding of how a neural network is trained with SGD: first four chapters; understanding of decision trees, LSTMs, and CNNs: first half; detailed understanding of how to build everything from scratch: whole book).

mudrockbestgirl 4y ago

> 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?

elephanlemon 4y ago

It’s a bit dated now, but the DeepLearning.ai GANs specialization covers topics through StyleGAN. If you have no experience with ML at all I would probably start with the first two courses of their Deep Learning specialization and then jump into the GANs specialization. I would also highly recommend FastAI’s Deep Learning for Coders (and their new course that came out this year). You’ll start immediately with some cool applications (basic image recognition and NLP) and then drill down from there to learn how they work in detail. It’s set up such that you can learn as much as you want (basics with no depth: first chapter; basic understanding of how a neural network is trained with SGD: first four chapters; understanding of decision trees, LSTMs, and CNNs: first half; detailed understanding of how to build everything from scratch: whole book).