18337
quickjs
18337 | quickjs | |
---|---|---|
14 | 66 | |
189 | 7,674 | |
3.2% | - | |
5.7 | 9.1 | |
about 1 year ago | 14 days ago | |
Jupyter Notebook | C | |
- | GNU General Public License v3.0 or later |
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18337
- Hello I wanted to know what would be the best way to get started in Julia and artificial intelligence. I looked around alot of different languages and saw Julia was good for data science and for artificial intelligence but would like to know what would be good ways to just do it. Thank you
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SciML/SciMLBook: Parallel Computing and Scientific Machine Learning (SciML): Methods and Applications (MIT 18.337J/6.338J)
This was previously the https://github.com/mitmath/18337 course website, but now in a new iteration of the course it is being reset. To avoid issues like this in the future, we have moved the "book" out to its own repository, https://github.com/SciML/SciMLBook, where it can continue to grow and be hosted separately from the structure of a course. This means it can be something other courses can depend on as well. I am looking for web developers who can help build a nicer webpage for this book, and also for the SciMLBenchmarks.
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Why Fortran is easy to learn
I would say Fortran is a pretty great language for teaching beginners in numerical analysis courses. The only issue I have with it is that, similar to using C+MPI (which is what I first learned with, well after a bit of Java), the students don't tend to learn how to go "higher level". You teach them how to write a three loop matrix-matrix multiplication, but the next thing you should teach is how to use higher level BLAS tools and why that will outperform the 3-loop form. But Fortran then becomes very cumbersome (`dgemm` etc.) so students continue to write simple loops and simple algorithms where they shouldn't. A first numerical analysis course should teach simple algorithms AND why the simple algorithms are not good, but a lot of instructors and tools fail to emphasize the second part of that statement.
On the other hand, the performance + high level nature of Julia makes it a rather excellent tool for this. In MIT graduate course 18.337 Parallel Computing and Scientific Machine Learning (https://github.com/mitmath/18337) we do precisely that, starting with direct optimization of loops, then moving to linear algebra, ODE solving, and implementing automatic differentiation. I don't think anyone would want to give a homework assignment to implement AD in Fortran, but in Julia you can do that as something shortly after looking at loop performance and SIMD, and that's really something special. Steven Johnson's 18.335 graduate course in Numerical Analysis (https://github.com/mitmath/18335) showcases some similar niceties. I really like this demonstration where it starts from scratch with the 3 loops and shows how SIMD and cache-oblivious algorithms build towards BLAS performance, and why most users should ultimately not be writing such loops (https://nbviewer.org/github/mitmath/18335/blob/master/notes/...) and should instead use the built-in `mul!` in most scenarios. There's very few languages where such "start to finish" demonstrations can really be showcased in a nice clear fashion.
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What are some interesting papers to read?
And why not take a course while you're at it.
- Composability in Julia: Implementing Deep Equilibrium Models via Neural Odes
- [2109.12449] AbstractDifferentiation.jl: Backend-Agnostic Differentiable Programming in Julia
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Is that true?
Here's a good one. It's in Julia but it should do the trick. The main instructor is the most prolific Julia dev in the world.
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[D] Has anyone worked with Physics Informed Neural Networks (PINNs)?
NeuralPDE.jl fully automates the approach (and extensions of it, which are required to make it solve practical problems) from symbolic descriptions of PDEs, so that might be a good starting point to both learn the practical applications and get something running in a few minutes. As part of MIT 18.337 Parallel Computing and Scientific Machine Learning I gave an early lecture on physics-informed neural networks (with a two part video) describing the approach, how it works and what its challenges are. You might find those resources enlightening.
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[P] Machine Learning in Physics?
It's a very thriving field. If you are interested in methods research and want to learn some of the techniques behind it, I would recommend taking a dive into my lecture notes as I taught a graduate course at MIT, 18.337 Parallel Computing and Scientific Machine Learning, specifically designed to get new students onboarded into this research program.
- MIT 18.337J: Parallel Computing and Scientific Machine Learning
quickjs
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What Is in a Rust Allocator?
You may be familiar, but I just wanted to show how it is available in many C implementations and is used, for example, in QuickJS: https://github.com/bellard/quickjs/blob/0c8fecab2392387d76a4...
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Show HN: Happy Pi day with this PWA to cut 100k Pi digits offline
It uses service workers to cache static files, by the time it opens up you already free to be offline, try toggle network switch to verify.
It has download link at bottom of the about page ([accdoo.app/about]) which you could then self host it by dropping into any static hosting services.
btw, the Pi feature was by-product from the original App but I won't expand here, if you'd like to learn more, please checkout its two Show HN post (39115559 and 39138957) previously.
[wiki]: https://en.wikipedia.org/wiki/Chudnovsky_algorithm
[quickjs/pi]: https://bellard.org/quickjs/pi.html
[pi_bigint.js]: https://github.com/bellard/quickjs/blob/master/examples/pi_b...
[accdoo.app/about]: https://accdoo.app/about#releases
[39115559]: https://news.ycombinator.com/item?id=39115559
[39138957]: https://news.ycombinator.com/item?id=39138957
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Ask HN: C/C++ plugin make JavaScipt end up with C/C++ binary?
Just go with quickjs, I think this is what you are looking for.
https://bellard.org/quickjs/
- Show HW: accdoo cipher web app now fused with offline Pi cutter (100k digits)
- QuickJS JavaScript Engine
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A list of JavaScript engines, runtimes, interpreters
QuickJS
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Can you make your own JavaScript by implementing ECMAScript standard?
I think QuickJS, written in C, is a user-"friendly" starting point for implementing ECMA-262. Documentation QuickJS Javascript Engine.
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New QuickJS Release
There is a readme on the project's main page: https://bellard.org/quickjs/
The newsworthy bit here is that the activity seemed to have stalled for year or two and now Fabrice pushed a few fixes and made a new release.
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GitHub
Just to demonstrate GitHub repositories do not necessarily reflect upon a programmers' body of work, Fabrice Bellard has one (1) repository published on GitHub, quickjs. Compare the list of work on Bellard's home page https://bellard.org/.
What are some alternatives?
DataDrivenDiffEq.jl - Data driven modeling and automated discovery of dynamical systems for the SciML Scientific Machine Learning organization
Duktape - Duktape - embeddable Javascript engine with a focus on portability and compact footprint
Vulpix - Fast, unopinionated, minimalist web framework for .NET core inspired by express.js
jerryscript - Ultra-lightweight JavaScript engine for the Internet of Things.
NeuralPDE.jl - Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
mjs - Embedded JavaScript engine for C/C++
SciMLTutorials.jl - Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.
edex-ui - A cross-platform, customizable science fiction terminal emulator with advanced monitoring & touchscreen support.
GPUCompiler.jl - Reusable compiler infrastructure for Julia GPU backends.
Nuitka - Nuitka is a Python compiler written in Python. It's fully compatible with Python 2.6, 2.7, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 3.10, and 3.11. You feed it your Python app, it does a lot of clever things, and spits out an executable or extension module.
BenchmarkTools.jl - A benchmarking framework for the Julia language
esp8266-quickjs - An attempt on getting QuickJS working on ESP8266 hardware