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Deep Learning Foundations to Stable Diffusion

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Re: Deep Learning Foundations to Stable Diffusion

#81

Post by bl0b »

nbzso wrote: post #10

This is cool, but I have a fundamental question. Why learn machine learning when all the machines will learn how to create and control themselves and powerful elites will offload the burden of governments to control us? Or we race ourselves to fulfill the last jobs on earth? Is this progress for humanity or enslavement and the end of our species? I am serious. This question is honest. Maybe my IQ is too low to unders…

I'm learning this stuff because I think it's fascinating.

To be fair I know I'm light years behind the cutting-edge so I feel I can sleep assured that I won't take too much of the blame for having created our new AI overlords, or taking the jobs, etc.

Also, generative art and LLMs are undoubtedly impressive, but not completely my cup of tea. But you can always take your ML knowledge and find a domain/problem you care about to apply it to. It's not like literally everything will be solved in the future.

Re: Deep Learning Foundations to Stable Diffusion

#82

Post by pvitz »

startup_99 wrote: post #37
jph00 wrote: post #4

Hi folks. Nice to see our new free (and ad-free) course here on HN! This course is for folks that are already comfortable training neural nets and understand the basic ideas of SGD, cross-entropy, embeddings, etc. It will help you both understand these foundations more deeply, since you'll be creating everything from scratch (i.e. from only Python and its standard library), and to understand modern generative modelin…

not to derail the conversation... I came across your APL study session videos while exploring the other material. I used APL professionally for about a decade back in the early 80's. I am always pleasantly surprised when I see interesting work being done in APL. Interestingly enough I always thought APL would eventually evolve into a central language for AI. There were attempts at designing hardware-based APL machine…

I have recently read "The Little Learner" and was quite amazed how much I knew already just because I know APL/J. With the right libraries, these vector languages would be perfect for neural network / deep learning tasks.

Re: Deep Learning Foundations to Stable Diffusion

#83

Post by Closi »

blibble wrote: post #76
Closi wrote: post #74

Earlier quoted context omitted.

Depends what people are motivated to do - if you just want to build cool things quicker, you will probably be excited by ML. If you like writing algorithms and enjoy the mental problem-solving aspect of it, then you might not like it. If your main motivation is to protect your job/livelihood and ensure your existing skillset is in demand, then you will probably be worried. But in any scenario, the cat is out of the b…

> But in any scenario, the cat is out of the bag - you can't un-invent it so might as well get excited and be on the train, rather than be the person that gets left behind. there are plenty of other ways to deal with it politically push for AI output to be banned, made un-exploitable or highly taxed or the luddite approach time will tell how the several billion people about to be made destitute will react

Pushing for AI output to be banned is not a long term approach IMO unless all countries do it in unison - countries who do not ban AI output will be much more competitive, and those that ban it will be left in the dust. Any country that banned computers in 1979 would have really hurt themselves.

Society will need to adapt - doesn't necessarily make sense having several billion people doing something that a computer can do quicker, more accurately and more easily, just to keep people in employment (doing something that a lot of people hate).

Re: Deep Learning Foundations to Stable Diffusion

#84

Post by epups »

nbzso wrote: post #64
narag wrote: post #40

Earlier quoted context omitted.

That's not a serious question, it's the very definition of trolling. You can replace Stable Diffusion with any other subject to learn, and that particular doom scenario with a similar one or, if you don't feel very inclined to ellucubrate, just say the good old "why learn anything when we all are eventually going to die and be forgotten". Not cool.

Please, tell me your definition of trolling. Maybe after you help me understand, I will explain to the illustrators why they must prompt with text instead of drawing? Assuming that I don't learn is wrong. I have a local installation of SD with a lot of models to test. The only useful thing in this gizmo is the Control Net module or maybe the Photoshop plugin for outpainting. You can upload your linear representation…

Humans did not stop learning to count after the calculator was invented.

Re: Deep Learning Foundations to Stable Diffusion

#85

Post by creata »

maelito wrote: post #30
nbzso wrote: post #10

This is cool, but I have a fundamental question. Why learn machine learning when all the machines will learn how to create and control themselves and powerful elites will offload the burden of governments to control us? Or we race ourselves to fulfill the last jobs on earth? Is this progress for humanity or enslavement and the end of our species? I am serious. This question is honest. Maybe my IQ is too low to unders…

I don't get why your question is getting unvoted. This is a serious question. For engineers and "normal people" as well : why bother learning ?

1. Because it's fun.

2. Because "all the machines will learn how to create and control themselves and powerful elites will offload the burden of governments to control us?" is a ridiculous sci-fi scenario, and the chatbots aren't nearly as good as so many people pretend they are.

Re: Deep Learning Foundations to Stable Diffusion

#86

Post by tempaccount420 »

jph00 wrote: post #53
amelius wrote: post #49

How much math does the reader need under their belt? Is high-school math sufficient?

Yes high school math is sufficient, as long as you did basic calculus (derivatives and the chain rule) and probability. We cover stuff that's well beyond high school math, but it's introduced when it's used, and we link to external resources for more details as needed.

> as long as you did basic calculus (derivatives and the chain rule) and probability

How much additional effort would it require to incorporate these topics into your course, potentially as supplementary content? This could make the course more accessible to students from countries with less comprehensive educational systems.

Re: Deep Learning Foundations to Stable Diffusion

#88

Post by rg111 »

mattyyeung wrote: post #29

I just finished this! My thoughts: I recommend it. I feel I can now read an arbitrary paper, frown a lot, and eventually understand what it's talking about - to the point where I can implement my own buggy version. And hey, I built my own stable diffusion!! I found the previous version of this course[1] to be a good complement: it's older (predates SD) but I feel it explains core concepts slightly better. Very unders…

I second the idea of doing older versions of Part-2 as well.

One will get much better grasp of DL concepts doing those versions rather than this.

E.g. 2019/20 version IIRC.

Re: Deep Learning Foundations to Stable Diffusion

#89

Post by ttul »

I audited this course - I say “audited” because I didn’t have time to do the homework; I just watched the lectures and spent what time I could playing around in Colab building some toy models. This is very much a full time university level course and you should treat it that way if you intend to complete it properly. You will need to reserve 10 hours per week for the homework - at least.

I enjoyed how Jeremy and his teaching assistants step through every detail so that you can understand how these fantastically complex systems actually work, from the ground up. Nothing is glossed over or taken for granted.

Much of the course material is building an AI programming framework from scratch. It’s well worth the hassle; I now at least have an intuition about how these systems work, rather than a glossed over impression with many holes. The other thing I greatly appreciated were the insights from other classmates in the forums. Some people way smarter than me took this course and Jeremy would incorporate their flashes of insight into the lectures.

I give this course a 10/10 and I hope my life gets a little easier so that I can get back to the lessons and finish the homework one day.

Re: Deep Learning Foundations to Stable Diffusion

#90

Post by Avalaxy »

ttul wrote: post #89

I audited this course - I say “audited” because I didn’t have time to do the homework; I just watched the lectures and spent what time I could playing around in Colab building some toy models. This is very much a full time university level course and you should treat it that way if you intend to complete it properly. You will need to reserve 10 hours per week for the homework - at least. I enjoyed how Jeremy and his…

> You will need to reserve 10 hours per week for the homework - at least.

Is that for part 2? Or also for part 1? How long did it take you, doing it 10 hours per week?

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