Flux (machine-learning framework)
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| This article, Flux (machine-learning framework), has recently been created via the Articles for creation process. Please check to see if the reviewer has accidentally left this template after accepting the draft and take appropriate action as necessary.
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Comment: While indications suggest that it may gain notability, it is not yet notable as far as I can tell. Additionally, I see no indication that the tone concerns were addressed. StudiesWorld (talk) 21:16, 3 July 2019 (UTC)
- Comment: All (except one) source is connected to the creators of this software. You need to find independent and reliable sources to show this machine learning software is notable. Dreamy Jazz 🎷 talk to me | my contributions 14:19, 2 June 2019 (UTC)
| Flux | |
|---|---|
 | |
| Original author(s) | Michael J Innes.[1] |
| Stable release | v0.8.3
|
| Repository | github |
| Written in | Julia |
| Type | Machine learning library |
| Website | https://fluxml.ai |
Flux is an open-source machine-learning library and ecosystem written completely in Julia.[1][2] Its current stable release is v0.8.3.[3] Flux takes full advantage of Julia's just-ahead-of-time compilation and exposes an intuitive and flexible interface to users, while still providing a layer-stacking-based interface for simpler models, and can be readily integrated with other Julia packages.[4] Flux can take full advantage of all Julia language features, and can work with almost all Julia packages. For example, GPU support is supplied transparently by CuArrays.jl, due to Julia's multiple dispatch.[5] This is in contrast to some other machine learning frameworks which are implemented in other languages with Julia bindings, such as TensorFlow.jl, and thus are more limited by the functionality present in the underlying implementation, which is often in C or C++.[6]
This advantage has been used, for example, to implement support for Neural Differential Equations, by fusing Flux and DifferentialEquations.jl into DiffEqFlux.jl[7][8].
Flux supports recurrent and convolutional networks. It is also capable of Differentiable programming[9][10][11] through its Automatic differentiation module, Tracker, or with the still experimental source-to-source automatic differentiation Zygote package[12]
Julia is among the most popular machine-learning languages in Github[13] and Flux is appointed as its most highly regarded machine-learning repository.[13] A demonstration[14] compiling Julia code to run in Google's Tensor processing unit received praise from Google Brain AI lead Jeff Dean.[15]
See also
References
- ^ a b Innes, Michael (2018-05-03). "Flux: Elegant machine learning with Julia". The Journal of Open Source Software. doi:10.21105/joss.00602. Retrieved 2019-06-02.
{{cite web}}: Cite has empty unknown parameter:|dead-url=(help) - ^ Innes, Mike; Bradbury, James; Fischer, Keno; Gandhi, Dhairya; Mariya Joy, Neethu; Karmali, Tejan; Kelley, Matt; Pal, Avik; Concetto Rudilosso, Marco; Saba, Elliot; Shah, Viral; Yuret, Deniz. "Building a Language and Compiler for Machine Learning". julialang.org. Retrieved 2019-06-02.
- ^ FluxML/Flux.jl v0.8.3, Flux, 2019-06-01, retrieved 2019-06-02
- ^ "Machine Learning and Artificial Intelligence". juliacomputing.com. Retrieved 2019-06-02.
{{cite web}}: Cite has empty unknown parameter:|dead-url=(help) - ^ Gandhi, Dhairya (2018-11-15). "Julia at NeurIPS and the Future of Machine Learning Tools". juliacomputing.com. Retrieved 2019-06-02.
{{cite web}}: Cite has empty unknown parameter:|dead-url=(help) - ^ "TensorFlow.jl: An Idiomatic Julia Front End for TensorFlow". The Journal of Open Source Software. 2018-11-01. doi:10.21105/joss.01002. Retrieved 2019-06-03.
- ^ Rackauckas, Chris; Innes, Mike; Ma, Yingbo; Bettencourt, Jesse; White, Lyndon; Dixit, Vaibhav (2019-02-06). "DiffEqFlux.jl - A Julia Library for Neural Differential Equations". arXiv.
- ^ "DiffEqFlux.jl – A Julia Library for Neural Differential Equations". julialang.org. Retrieved 2019-07-05.
- ^ "Flux – Reinforcement Learning vs. Differentiable Programming". fluxml.ai. Retrieved 2019-06-02.
- ^ "Flux – What Is Differentiable Programming?". fluxml.ai. Retrieved 2019-06-02.
- ^ Heath, Nick (December 6, 2018). "Julia vs Python: Which programming language will rule machine learning in 2019?". TechRepublic. Retrieved 2019-06-03.
{{cite web}}: Cite has empty unknown parameter:|dead-url=(help) - ^ Innes, Michael (2018-10-18). "Don't Unroll Adjoint: Differentiating SSA-Form Programs".
{{cite journal}}: Cite journal requires|journal=(help) - ^ a b Heath, Nick (January 25, 2019). "GitHub: The top 10 programming languages for machine learning". TechRepublic. Retrieved 2019-06-03.
{{cite web}}: Cite has empty unknown parameter:|dead-url=(help) - ^ Saba, Elliot; Fischer, Keno (2018-10-23). "Automatic Full Compilation of Julia Programs and ML Models to Cloud TPUs".
{{cite journal}}: Cite journal requires|journal=(help) - ^ Dean, Jeff [@JeffDean] (2018-10-23). "Julia + TPUs = fast and easily expressible ML computations" (Tweet). Retrieved 2019-06-02 – via Twitter.
Category:Deep learning Category:Free science software Category:Machine learning Category:Software stubs Category:Data mining and machine learning software Category:Free software programmed in Julia Category:Software using the MIT license