model-zoo
Flux.jl
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model-zoo | Flux.jl | |
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5 | 22 | |
883 | 4,391 | |
1.2% | 1.0% | |
4.6 | 8.7 | |
about 1 month ago | 5 days ago | |
Julia | Julia | |
GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
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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.
model-zoo
- sci-kit learn best for machine learning with Julia?
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Are there anyway to translate IPA to English? atleast, IPA text to pronounce sound
If your task is limited to isolated words or words that are separated by spaces, you can reverse the [CMU Pronouncing Dictionary](www.speech.cs.cmu.edu/cgi-bin/cmudict). In a programmatic environment with a dictionary/hash map, you will need to have the values be an extensible list of some sort to account for homophones. You could also train an ML model to convert phonemes to graphemes, like reversing this neural network model.
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Project in julia
You can also do some deep learning in Julia if you like. Flux.jl is Julia's deep learning library, and they have a model zoo of easy to follow working examples as a good starting point.
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Simple neural network model in Flux.jl doesn't seem to update the loss function
Secondly, flux.train! computes epochs automatically and is designed to take the entire data set. If you want to use the DataLoader, I think you probably need to write a custom training loop. It's not that complicated thankfully. The model zoo has some nice examples like: https://github.com/FluxML/model-zoo/blob/master/vision/mlp_mnist/mlp_mnist.jl
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Any Machine Learning Projects to Make with Julia
Check out the Flux.jl model zoo: https://github.com/FluxML/model-zoo#examples-listing
Flux.jl
- Julia 1.10 Released
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What Apple hardware do I need for CUDA-based deep learning tasks?
If you are really committed to running on Apple hardware then take a look at Tensorflow for macOS. Another option is the Julia programming language which has very basic Metal support at a CUDA-like level. FluxML would be the ML framework in Julia. I’m not sure either option will be painless or let you do everything you could do with a Nvidia GPU.
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[D] ClosedAI license, open-source license which restricts only OpenAI, Microsoft, Google, and Meta from commercial use
Flux dominance!
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What would be your programming language of choice to implement a JIT compiler ?
I’m no compiler expert but check out flux and zygote https://fluxml.ai/ https://fluxml.ai/
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Any help or tips for Neural Networks on Computer Clusters
I would suggest you to look into Julia ecosystem instead of C++. Julia is almost identical to Python in terms of how you use it but it's still very fast. You should look into flux.jl package for Julia.
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[D] Why are we stuck with Python for something that require so much speed and parallelism (neural networks)?
Give Julia a try: https://fluxml.ai
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Deep Learning With Flux: Loss Doesn't Converge
2) Flux treats softmax a little different than most other activation functions (see here for more details) such as relu and sigmoid. When you pass an activation function into a layer like Dense(3, 32, relu), Flux expects that the function is broadcast over the layer's output. However, softmax cannot be broadcast as it operates over vectors rather than scalars. This means that if you want to use softmax as the final activation in your model, you need to pass it into Chain() like so:
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“Why I still recommend Julia”
Can you point to a concrete example of one that someone would run into when using the differential equation solvers with the default and recommended Enzyme AD for vector-Jacobian products? I'd be happy to look into it, but there do not currently seem to be any correctness issues in the Enzyme issue tracker that are current (3 issues are open but they all seem to be fixed, other than https://github.com/EnzymeAD/Enzyme.jl/issues/278 which is actually an activity analysis bug in LLVM). So please be more specific. The issue with Enzyme right now seems to moreso be about finding functional forms that compile, and it throws compile-time errors in the event that it cannot fully analyze the program and if it has too much dynamic behavior (example: https://github.com/EnzymeAD/Enzyme.jl/issues/368).
Additional note, we recently did a overhaul of SciMLSensitivity (https://sensitivity.sciml.ai/dev/) and setup a system which amounts to 15 hours of direct unit tests doing a combinatoric check of arguments with 4 hours of downstream testing (https://github.com/SciML/SciMLSensitivity.jl/actions/runs/25...). What that identified is that any remaining issues that can arise are due to the implicit parameters mechanism in Zygote (Zygote.params). To counteract this upstream issue, we (a) try to default to never default to Zygote VJPs whenever we can avoid it (hence defaulting to Enzyme and ReverseDiff first as previously mentioned), and (b) put in a mechanism for early error throwing if Zygote hits any not implemented derivative case with an explicit error message (https://github.com/SciML/SciMLSensitivity.jl/blob/v7.0.1/src...). We have alerted the devs of the machine learning libraries, and from this there has been a lot of movement. In particular, a globals-free machine learning library, Lux.jl, was created with fully explicit parameters https://lux.csail.mit.edu/dev/, and thus by design it cannot have this issue. In addition, the Flux.jl library itself is looking to do a redesign that eliminates implicit parameters (https://github.com/FluxML/Flux.jl/issues/1986). Which design will be the one in the end, that's uncertain right now, but it's clear that no matter what the future designs of the deep learning libraries will fully cut out that part of Zygote.jl. And additionally, the other AD libraries (Enzyme and Diffractor for example) do not have this "feature", so it's an issue that can only arise from a specific (not recommended) way of using Zygote (which now throws explicit error messages early and often if used anywhere near SciML because I don't tolerate it).
So from this, SciML should be rather safe and if not, please share some details and I'd be happy to dig in.
- Flux: The Elegant Machine Learning Stack
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Jax vs. Julia (Vs PyTorch)
> In his item #1, he links to https://discourse.julialang.org/t/loaderror-when-using-inter... The issue is actually a Zygote bug, a Julia package for auto-differentiation, and is not directly related to Julia codebase (or Flux package) itself. Furthermore, the problematic code is working fine now, because DiffEqFlux has switched to Enzyme, which doesn't have that bug. He should first confirm whether the problem he is citing is actually a problem or not.
> Item #2, again another Zygote bug.
If flux chose a buggy package as a dependency, that's on them, and users are well justified in steering clear of Flux if it has buggy dependencies. As of today, the Project.toml for both Flux and DiffEqFlux still lists Zygote as a dependency. Neither list Enzyme.
https://github.com/FluxML/Flux.jl/blob/master/Project.toml
What are some alternatives?
Transformers.jl - Julia Implementation of Transformer models
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
NaiveGAflux.jl - Evolve Flux networks from scratch!
Knet.jl - Koç University deep learning framework.
AlphaZero.jl - A generic, simple and fast implementation of Deepmind's AlphaZero algorithm.
tensorflow - An Open Source Machine Learning Framework for Everyone
reproduced_data
FastAI.jl - Repository of best practices for deep learning in Julia, inspired by fastai
Torch.jl - Sensible extensions for exposing torch in Julia.
Lux.jl - Explicitly Parameterized Neural Networks in Julia
flax - Flax is a neural network library for JAX that is designed for flexibility.