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Deep Learning Foundations to Stable Diffusion

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Re: Deep Learning Foundations to Stable Diffusion

#101

Post by tomcam »

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…

Not criticizing you, here, just asking a sincere question.

It was obvious to me on first encounter that APL would never become widespread. Its character set was too abstruse for most programmers and at the time required a special monitor. Plus it seemed hard to maintain if you weren’t either a mathematician or a full-time APL developer.

Your thoughts?

Re: Deep Learning Foundations to Stable Diffusion

#102

Post by tomcam »

bravura wrote: post #58
frabia wrote: post #44

Earlier quoted context omitted.

Maybe odd question, but: would you recommend taking this course if my goal is to build (and sell) products leveraging ML? (e.g. SaaS) As in, with the pace of improvements from other AI startups and general availability of their APIs (e.g. GPT-4), is there a specific advantage (aside from maybe cost) to learning to build my own models? Or is the course more suitable for people wanting to become ML engineers (or simila…

Just my two cents, as someone with 8000 academic citations and one AI SaaS exit, on AI entrepreneurship: The more time you spend on marketing, the better. We found that as our AI got worse, our product got better.

> We found that as our AI got worse, our product got better.

I’m not smart enough to decode this. I can imagine multiple conflicting answers.

Re: Deep Learning Foundations to Stable Diffusion

#103

Post by tomcam »

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…

Damn good (and wryly funny) assessment. Thanks much!

Re: Deep Learning Foundations to Stable Diffusion

#104

Post by ttul »

Avalaxy wrote: post #90
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?

I didn't take part 1, so I can't comment intelligently on this.

Re: Deep Learning Foundations to Stable Diffusion

#105

Post by bravura »

frabia wrote: post #63
bravura wrote: post #58

Earlier quoted context omitted.

Just my two cents, as someone with 8000 academic citations and one AI SaaS exit, on AI entrepreneurship: The more time you spend on marketing, the better. We found that as our AI got worse, our product got better.

Thanks for your reply, though I'm not sure if I get your comment correctly. As in, I agree that marketing is super important but if I had an idea about a certain SaaS product that requires a certain ML model, I need to decide either to build it myself, or using somebody else's APIs. > We found that as our AI got worse, our product got better. Interesting. Could you elaborate on this too?

I guess my last sentence was confusing. What I meant was that we fell into the common trap that scientists want to do as fancy science as possible when they leave academia and enter the startup world. Because there's an issue of pride and desire for uniqueness. Whereas good business is not like art, where the mandate is to be as creative and unique as possible. The analogy from sports (which I don't watch) applicable to business is that teams just copy each other's plays and focus on out-executing each other.

So, specifically, our startup started with the idea that high-brow tech would be a key differentiator and give the best user experience. This is the common story trotted out by survivorship bias stories that make good tech news articles.

Whereas making cuts to our R&D time and focusing on UI/UX and working with simpler science ultimately led to better product.

From consulting, sales, and corporate work, one learns that the dirty secret of big-iron large tech companies is that all stuff sold as ML is just nicely packaged logistic regression. Or it was was five years ago. Nowadays I guess it would MAYBE be transformers, but the point being that off-the-shelf ML with solid data engineering work is what drives 99% of good products. Rarely is it truly innovative tech. I think Pete Skomoroch was the one that joked: "People say I'm a data scientist. I'm actually a data plumber."

I guess one could take this lesson from science. What you learn from a good PhD advisor is: a) read the latest work b) note the simple baseline approach constantly trashed as scoring 2% worse than the sophisticated intricate new things proposed and c) implement the simple baseline. Achieve impact not by hillclimbing on a standard metric but define a new problem or arbitrage insights from adjacent fields, etc.

Re: Deep Learning Foundations to Stable Diffusion

#106

Post by bwanab »

tomcam wrote: post #101
startup_99 wrote: post #37

Earlier quoted context omitted.

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…

Not criticizing you, here, just asking a sincere question. It was obvious to me on first encounter that APL would never become widespread. Its character set was too abstruse for most programmers and at the time required a special monitor. Plus it seemed hard to maintain if you weren’t either a mathematician or a full-time APL developer. Your thoughts?

Not OP, but I'll say you're right, but for the wrong reasons. Having embarked on learning APL by doing a project in it, the character set becomes second nature in a few days time. No special monitor needed, at least on my Mac, just a new font and it all worked flawlessly.

What caused me to hang up my glyphs were the inconsistencies and head scratching behavior of the language in so many corner cases. Working with very nice mentors I found that one could get around them, but because of the legacy of the language they have to be kept around to support existing code bases.

I loved the notion of having a language that gave a first class experience with matrices, though, and after looking around the space, I finally came to Julia and have been very happy.

Re: Deep Learning Foundations to Stable Diffusion

#107

Post by [deleted] »

nbzso wrote: post #17
jstx1 wrote: post #13

Earlier quoted context omitted.

This question isn't specific to machine learning - if you've given up already, don't bother learning anything and see how it goes.

I never give up. Just asked for information, to fight my own bias. Obviously there is a world outside which will have analogue and mechanical parts. But this is not the question. The question is why to cut your wrists with a blunt object and stream the event for validation is suddenly a rational behavior?

[deleted]

Re: Deep Learning Foundations to Stable Diffusion

#108

Post by sine_break »

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…

[dead]

Re: Deep Learning Foundations to Stable Diffusion

#109

Post by blibble »

Closi wrote: post #83
blibble wrote: post #76

Earlier quoted context omitted.

> 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 c…

> 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.

this argument is extremely similar to that which was used against proposals to ban slavery

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