amkkma

14 ms·

91.
by amkkma 5y ago
Thanks, I see where you are coming from. >It should be the responsibility of the developer of "model" to select the "fit" algorithm appropriate for "model." (They don't have to implement it, but they do
92.
by amkkma 5y ago
>I’ve seen a cult-like behavior in Julia community that is borderline toxic. Sorry, it’s the truth and needs to be said. Speed isn’t everything and people are magnetized by the benchmarks on the Julia website, especially non-software eng
93.
by amkkma 5y ago
Thanks for sharing! I think much of it has to do with the version you're stuck on. (ie startup and compile times are way way better with 1.6) Python having a broader ecosystem is a very good point, but I've found pycall to be very
94.
by amkkma 5y ago
I really don't understand your point about package management. Instead of Pip, virtualenv, conda, etc etc there's one package manager that resolves and installs native and binary dependencies, ensures reproducibility with human re
95.
by amkkma 5y ago
How is "model.fit(X,Y)" better than "fit!(model,X,Y)"? Julia is object oriented in a broad sense, it just uses multiple dispatch which is strictly more expressive than single dispatch, so doesn't make sense to have
96.
by amkkma 5y ago
>These features aren't of interest to practicing statisticians, which the parent comment was talking about. It's pretty convenient for things like uncertainty propagation and data cleaning...all things statisticians should care
97.
by amkkma 5y ago
Hi. Good question. This is addressed in several places in the comment thread by Keno and Chris.
98.
by amkkma 5y ago
It exists in the form of https://juliahub.com/lp/
99.
by amkkma 5y ago
>Python is far better suited for this. Similarly, if you want to produce standalone executables, C++, Rust, Go or Nim are better. That's the case now, because Julia made a design decision to focus on extreme composability, dynamism,
100.
by amkkma 5y ago
> Some of these languages support abstractions which are strictly equivalent to static multi-method dispatch Yes, and even with languages that have these abstractions, they aren't as pervasive as in Julia. Ex. There's a fundame
101.
by amkkma 5y ago
In addition to the comment about df.jl catching up, they aren't comparable at all. Julia's DF library is generic and allows user defined ops and types. You can put in GPU vectors, distributed vectors, custom number types etc. Jul
102.
by amkkma 5y ago
I believe we are currently at pytorch parity (and sometimes better) for speed. Memory usage depends....And this is without the extensive upcoming compiler improvements. The reason for the lag is that Julia has been focusing on general com
103.
by amkkma 5y ago
It's already superior to R for data munging stuff, imo https://twitter.com/evalparse/status/1416039770833096706 And https://github.com/JuliaPlots/AlgebraOfGraphics.jl >>> GoG
104.
by amkkma 5y ago
> As soon as you take Julia out of notebooks and try to build moderately complex apps with it, you realize how much you miss Python Why's that? What features or lack thereof of Julia contribute to that experience?
105.
Multiple Dispatch vs. Multi Parameter Function Overloading(youtube.com)
1 points by amkkma 5y ago|0 comments
106.
Efficient and safe approaches to mutation in data parallelism(juliafolds.github.io)
1 points by amkkma 5y ago|0 comments
107.
by amkkma 5y ago
"I see a non-negligible number of false claims and false promises on Julia's performance on the web every now and then. and when people test it for real production code they get disappointed with it." Can you cite one or more
108.
PClean: Bayesian Data Cleaning at Scale with Domain-Specific Prob Programming(twitter.com)
1 points by amkkma 6y ago|0 comments
109.
by amkkma 6y ago
When was this and what were the issues?
110.
MLJ.jl: A Julia package for composable machine learning(alan-turing-institute.github.io)
234 points by amkkma 6y ago|56 comments
111.
PlutoCon 2021(plutojl.org)
40 points by amkkma 6y ago|6 comments
112.
by amkkma 6y ago
Awesome resource The website is built in julia with https://github.com/tlienart/Franklin.jl , Cool! Would be interesting to have it teach DL in Julia as well.
113.
by amkkma 6y ago
Symbolics.jl is keeping things generic from the getgo.
114.
by amkkma 6y ago
Julia solves this by using interfaces (functions defined over one or more custom types that abstract over concrete information). Just overload those on your type, and voila it works with the CAS.
115.
by amkkma 6y ago
People definitely have used PyCall in production. Julia at this point covers everything in NumPy, SciPy and much more. For optimization, bayesian stuff, scientific, and the convergence of the above with ML, it's far ahead- https:&#x2F
116.
by amkkma 6y ago
Were you doing anything with: 1. custom units 2. custom GPU kernels 3. Custom array types 4. custom bayesian priors. 5. AD through custom types 6. Task based parallelism 7. symbolic gradients with modeling toolkit 8. Agent based modelin
117.
MIT's introduction to computational thinking Spring 2021(computationalthinking.mit.edu)
5 points by amkkma 6y ago|0 comments
118.
Jet.jl: A WIP compile time type checker for Julia(github.com)
146 points by amkkma 6y ago|19 comments
119.
by amkkma 6y ago
What do you know about the Julia ecosystem?
120.
by amkkma 6y ago
Julia is now the only viable next gen ML language

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