ml5-library
jax
ml5-library | jax | |
---|---|---|
16 | 82 | |
6,363 | 28,082 | |
0.5% | 2.0% | |
0.0 | 10.0 | |
5 months ago | 1 day ago | |
JavaScript | Python | |
GNU General Public License v3.0 or later | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
ml5-library
- Why do people curse JS so much, but also say it's better than Python
-
Riffr - Create Photo Montages in the Browser with some ML Magic✨
Important APIs - ml5 for in-browser detection, face-api that uses tensorflow-node to accelerate on-server detection. VueUse for a bunch of useful component tools like the QR Code generator. Yahoo's Gifshot for creating gif files in-browser etc.
-
Contributing to WebSockets – Cryptocurrency Users
> Have we seen any creator of a deep learning library, take a similar position if not stopping any support for anyone using it for mass surveillance?
ml5.js license:
> This license gives everyone as much permission to work with this software as possible as long as they comply with the ml5.js Code of Conduct [...]
ml5.js code of conduct:
> Do not: [...] Use ml5.js to build tools of mass surveillance and prediction to repress the rights of people
https://github.com/ml5js/ml5-library/blob/main/LICENSE.md
Not sure how enforcable this is but it exists.
-
Brain.js: GPU Accelerated Neural Networks in JavaScript
See also: https://ml5js.org/
"The library provides access to machine learning algorithms and models in the browser, building on top of TensorFlow.js with no other external dependencies."
-
10 Mind Blowing JavaScript libraries Of 2022 (I mean it Javascript Noob)
(5) ml5.js
-
Top 5 JavaScript Libraries for Machine Learning, Deep Learning
ML.js
-
[Showoff Saturday] I made a captcha prototype that requires a banana
I used ml5js.org , p5js.org and https://teachablemachine.withgoogle.com to train the Banana images. When you create a new image project on Teachable Machine, you can output the p5js and basically use it right out of the box - I customized js, css, and html from there.
-
My First 30 Days of 100 Days of Code.
Going forward: I'll be 100% into JavaScript. You can use JavaScript in so many fields nowadays. Websites React, Mobile Apps React Native, Machine Learning TensorFlow & ML5, Desktop Applications Electron, and of course the backend Node as well. It's kind of a no-brainer. Of course, they all have specific languages that are better, but for now, JavaScript is a bit of a catch-all.
-
PyTorch vs. TensorFlow in 2022
Yeah they made ml5.js for this reason: https://ml5js.org/
I do feel like Google could do better communicating all of their different tools though. Their ecosystem is large and pretty confusing - they've got so many projects going on at once that it always seems like everyone gets fed up with them before they take a second pass and make them more friendly to newcomers.
Facebook seems to have taken a much more focused approach as you can see with PyTorch Live
-
[D] Are you using PyTorch or TensorFlow going into 2022?
From other comments, a lot of JavaScript developers who want to use TensorFlow had never heard of TensorFlow.js or ml5.js!
jax
-
The Elements of Differentiable Programming
The dual numbers exist just as surely as the real numbers and have been used well over 100 years
https://en.m.wikipedia.org/wiki/Dual_number
Pytorch has had them for many years.
https://pytorch.org/docs/stable/generated/torch.autograd.for...
JAX implements them and uses them exactly as stated in this thread.
https://github.com/google/jax/discussions/10157#discussionco...
As you so eloquently stated, "you shouldn't be proclaiming things you don't actually know on a public forum," and doubly so when your claimed "corrections" are so demonstrably and totally incorrect.
-
Julia GPU-based ODE solver 20x-100x faster than those in Jax and PyTorch
On your last point, as long as you jit the topmost level, it doesn't matter whether or not you have inner jitted functions. The end result should be the same.
Source: https://github.com/google/jax/discussions/5199#discussioncom...
-
Apple releases MLX for Apple Silicon
The design of MLX is inspired by frameworks like NumPy, PyTorch, Jax, and ArrayFire.
-
MLPerf training tests put Nvidia ahead, Intel close, and Google well behind
I'm still not totally sure what the issue is. Jax uses program transformations to compile programs to run on a variety of hardware, for example, using XLA for TPUs. It can also run cuda ops for Nvidia gpus without issue: https://jax.readthedocs.io/en/latest/installation.html
There is also support for custom cpp and cuda ops if that's what is needed: https://jax.readthedocs.io/en/latest/Custom_Operation_for_GP...
I haven't worked with float4, but can imagine that new numerical types would require some special handling. But I assume that's the case for any ml environment.
But really you probably mean fixed point 4bit integer types? Looks like that has had at least some work done in Jax: https://github.com/google/jax/issues/8566
-
MatX: Efficient C++17 GPU numerical computing library with Python-like syntax
>
Are they even comparing apples to apples to claim that they see these improvements over NumPy?
> While the code complexity and length are roughly the same, the MatX version shows a 2100x over the Numpy version, and over 4x faster than the CuPy version on the same GPU.
NumPy doesn't use GPU by default unless you use something like Jax [1] to compile NumPy code to run on GPUs. I think more honest comparison will mainly compare MatX running on same CPU like NumPy as focus the GPU comparison against CuPy.
[1] https://github.com/google/jax
-
JAX – NumPy on the CPU, GPU, and TPU, with great automatic differentiation
Actually that never changed. The README has always had an example of differentiating through native Python control flow:
https://github.com/google/jax/commit/948a8db0adf233f333f3e5f...
The constraints on control flow expressions come from jax.jit (because Python control flow can't be staged out) and jax.vmap (because we can't take multiple branches of Python control flow, which we might need to do for different batch elements). But autodiff of Python-native control flow works fine!
-
Julia and Mojo (Modular) Mandelbrot Benchmark
For a similar "benchmark" (also Mandelbrot) but took place in Jax repo discussion: https://github.com/google/jax/discussions/11078#discussionco...
-
Functional Programming 1
2. https://github.com/fantasyland/fantasy-land (A bit heavy on jargon)
Note there is a python version of Ramda available on pypi and there’s a lot of FP tidbits inside JAX:
3. https://pypi.org/project/ramda/ (Worth making your own version if you want to learn, though)
4. For nested data, JAX tree_util is epic: https://jax.readthedocs.io/en/latest/jax.tree_util.html and also their curry implementation is funny: https://github.com/google/jax/blob/4ac2bdc2b1d71ec0010412a32...
Anyway don’t put FP on a pedestal, main thing is to focus on the core principles of avoiding external mutation and making helper functions. Doesn’t always work because some languages like Rust don’t have legit support for currying (afaik in 2023 August), but in those cases you can hack it with builder methods to an extent.
Finally, if you want to understand the middle of the midwit meme, check out this wiki article and connect the free monoid to the Kleene star (0 or more copies of your pattern) and Kleene plus (1 or more copies of your pattern). Those are also in regex so it can help you remember the regex symbols. https://en.wikipedia.org/wiki/Free_monoid?wprov=sfti1
The simplest example might be {0}^* in which case
0: “” // because we use *
-
Best Way to Learn JAX
Hello! I'm trying to learn JAX over the next couple of weeks. Ideally, I want to be comfortable with using it for projects after about 3 weeks to a month, although I understand that may not be realistic. I currently have experience with PyTorch and TensorFlow. How should I go about learning JAX? Is there a specific YouTube tutorial or online course I should use, or should I just use the tutorial on https://jax.readthedocs.io/? Any information, advice, or experience you can share would be much appreciated!
- Codon: Python Compiler
What are some alternatives?
tfjs-models - Pretrained models for TensorFlow.js
Numba - NumPy aware dynamic Python compiler using LLVM
handpose-facemesh-demos - 🎥🤟 8 minimalistic templates for tfjs mediapipe handpose and facemesh
functorch - functorch is JAX-like composable function transforms for PyTorch.
hal9ai - Hal9 — Data apps powered by code and LLMs [Moved to: https://github.com/hal9ai/hal9]
julia - The Julia Programming Language
maze-lightning - This simple project approximates the shape of lightning by generating a random maze using Randomized Prim's algorithm and solving it using breadth-first search.
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
bias-monitor - A Chrome Extension that promotes politically diverse news reading with Artificial Intelligence!
Cython - The most widely used Python to C compiler
pyodide - Pyodide is a Python distribution for the browser and Node.js based on WebAssembly
jax-windows-builder - A community supported Windows build for jax.