opensandwich (u/opensandwich)

opensandwich commented on Z-Image: Powerful and highly efficient image generation model with 6B parameters github.com/Tongyi-MAI/Z-I... · Posted by u/doener

sheepscreek · 3 months ago

Apparently - https://github.com/ivanfioravanti/z-image-mps

Supports MPS (Metal Performance Shaders). Using something that skips Python entirely along with a mlx or gguf converted model file (if one exists) will likely be even faster.

opensandwich · 3 months ago

(Not tested) though apparently it already exists: https://github.com/leejet/stable-diffusion.cpp/wiki/How-to-U...

opensandwich commented on I’m Not a Robot neal.fun/not-a-robot/... · Posted by u/meetpateltech

sedatk · 6 months ago

I gave up at "Mark all squares of the 64th floor of the Empire State Building"[1]. I had even spent an hour on the chess challenge, but that one looked tasteless if I wasn't missing a trick. I thought that I was supposed to try all the floors close to 64 (considering the topmost one below the tower was 86). I really didn't have the patience for that, especially the possibility of missing to mark an overflowing pixel line, etc.

Also, I hit a bug at the AI challenge which prevented me to pass it. So I had to spent at least 5-6 more tries to pass it.[2]

Fun, but wouldn't go near it again :)

[1] https://bsky.app/profile/ssg.dev/post/3lz3gxm42jc2w

[2] https://bsky.app/profile/ssg.dev/post/3lz2o4mdrzc2l

opensandwich · 6 months ago

They gave me 2 queens to start with on my second attempt

opensandwich commented on I’m Not a Robot neal.fun/not-a-robot/... · Posted by u/meetpateltech

smusamashah · 6 months ago

Stuck at diamond axe. This recipe isn't being accepted https://minecraft.wiki/w/Diamond_Axe

opensandwich · 6 months ago

Try the pickaxe

opensandwich commented on How to become a pure mathematician or statistician (2008) hbpms.blogspot.com/... · Posted by u/ipnon

opensandwich · 6 months ago

Wow, this is a wild ride. I remember coming across this page because the author was from my alma mater and we were pursing the same (undergrad) degree. At the time, we could do a double major in Pure Mathematics and Statistics so long as we completed the coursework requirements, which is probably why that page even exists.

The page is ~15 years old now, and I think it should be read as though its written by a 22 yr old, more reflecting on their recent university education than a guide to how to become a working mathematician.

---

With that note, I would say if someone is eager to engage in mathematics and statistics _at an undergrad level_ (at the time at my university, it was _unusual_ for people to pursue machine learning as a major, and it was in computer science school). I would recommend really focussing on Real Analysis, and the higher statistics courses, try to find the links and the commonality between the proofs and the key ideas. I would also tell myself to not to shy away from martingale theory and link it to measure theory.

Pure mathematics is a weird world. In the moment I hated myself for choosing it in undergrad, it absolutely tanked my grades because of the weird mental state I was in. At the same time when I got to my PhD/research everything starting really started to click. It's immensely difficult to digest and consume all the content in the 12-14 odd weeks that the coursework typically demands.

opensandwich commented on Bayesian Statistics: The three cultures statmodeling.stat.columbi... · Posted by u/luu

tfehring · 2 years ago

The author is claiming that Bayesians vary along two axes: (1) whether they generally try to inform their priors with their knowledge or beliefs about the world, and (2) whether they iterate on the functional form of the model based on its goodness-of-fit and the reasonableness and utility of its outputs. He then labels 3 of the 4 resulting combinations as follows:

    ┌───────────────┬───────────┬──────────────┐
    │               │ iteration │ no iteration │
    ├───────────────┼───────────┼──────────────┤
    │ informative   │ pragmatic │ subjective   │
    │ uninformative │     -     │ objective    │
    └───────────────┴───────────┴──────────────┘

My main disagreement with this model is the empty bottom-left box - in fact, I think that's where most self-labeled Bayesians in industry fall:

- Iterating on the functional form of the model (and therefore the assumed underlying data generating process) is generally considered obviously good and necessary, in my experience.

- Priors are usually uninformative or weakly informative, partly because data is often big enough to overwhelm the prior.

The need for iteration feels so obvious to me that the entire "no iteration" column feels like a straw man. But the author, who knows far more academic statisticians than I do, explicitly says that he had the same belief and "was shocked to learn that statisticians didn’t think this way."

opensandwich · 2 years ago

As someone who isn't particularly well-versed in Bayesian "stuff". Does Bayesian non-parametric methods fall under "uninformative" + "iteration" approach?

I have a feeling I'm just totally barking up the wrong tree, but don't know where my thinking/understanding is just off.

opensandwich commented on Treebomination: Convert a scikit-learn decision tree into a Keras model github.com/Dobiasd/treebo... · Posted by u/Dobiasd

opensandwich · 3 years ago

Just an FYI - you can achieve this in 3 layers. It does not need to be deep. https://github.com/charliec443/TreeGrad

opensandwich commented on Want to be an actuary? Odds are, you’ll fail the test wsj.com/articles/actuary-... · Posted by u/pondsider

opensandwich · 4 years ago

Another actuary checking in from Institute of Actuaries Australia - I'm a "qualified" actuary (associate level) who decided to not even try for the fellowship exams and instead change course completely and do a PhD in Machine Learning.

No one outside of the actuarial industry cares or really knows what it means to be an actuary. Having an actuarial background in non-traditional actuarial areas is almost more of a curse than a blessing as people don't really know what to do with you. Furthermore actuaries seem to demand a premium for a cohort that don't have strong enough grounding to do ML research or enough development chops to be an ML engineer. So you end up competing with other people in the data science field...It really is a weird position to be in.

opensandwich commented on The Invention of Chinese historytoday.com/archive/... · Posted by u/Thevet

ncmncm · 4 years ago

Primarily Mandarin speakers I have known were taught, and believed, that the "dialects" were basically the same as Mandarin, just with different pronunciation.

They sincerely believed that when (e.g.) Cantonese speakers read and wrote, they were not wholesale translating from and to Mandarin, but simply, when writing, transcribing Cantonese using the universal ideographic calligraphy.

In fact, writing in dialect languages is not taught. The extremely elaborate system dictating which of usually several, often many syllabary characters that sound identical must be used in writing a word in Mandarin (very commonly mistaken for ideographic writing) cannot work for the other sinitic languages.

(Sinitic languages admit about 1200 distinct syllables, but the syllabary writing system uses many times that number, so many necessarily sound alike. Mandarin speakers are taught that the characters are not merely syllables with attached historical rules, but ideograms that represent distinct thoughts. (Numerous just-so examples are used to support the notion.) This has often led to belief that ancient documents using the characters could be read and understood without deep knowledge of the actual language and world of the writer, resulting in, at best, comical translations.)

opensandwich · 4 years ago

You are definitely correct in saying that in general people view it as simply changing the pronunciations.

Though I would also say that people also appreciate the nuances. There are comedic sketches on this: https://www.youtube.com/watch?v=tK51dlpqpSE (~5.30)

opensandwich commented on Course: Mathematics for machine learning coursera.org/specializati... · Posted by u/nafizh

p1esk · 8 years ago

Many ML methods require solid knowledge of probability and statistics. Strangely this course does not cover that.

opensandwich · 8 years ago

I agree - it looks equivalent to perhaps 1st year mathematics in Australian university or 2nd year 1st semester if I'm being really generous.

This definitely isn't sufficient for machine learning, but it is a start.

opensandwich commented on Michelangelo: Uber’s Machine Learning Platform eng.uber.com/michelangelo... · Posted by u/holografix

aorloff · 9 years ago

I am surprised by this - h2o.ai seems to have many if not all of the features of this, and is open source.

"Specifically, there were no systems in place to build reliable, uniform, and reproducible pipelines for creating and managing training and prediction data at scale. Prior to Michelangelo, it was not possible to train models larger than what would fit on data scientists’ desktop machines, and there was neither a standard place to store the results of training experiments nor an easy way to compare one experiment to another."

Seems like h2o.ai fits a lot of that bill.

opensandwich · 9 years ago

h2o.ai doesn't really do data pipelines, though it does appear they are eager to go into this space through their new driverless.ai tool. However this does not appear to be open source.