Difference between revisions of "Differentiable Programming"
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* [[Automated Machine Learning (AML) - AutoML]] | * [[Automated Machine Learning (AML) - AutoML]] | ||
* [http://medium.com/syncedreview/julia-computing-mit-introduce-differentiable-programming-system-bridging-ai-and-science-738c8a9eb0b9 Julia Computing & MIT Introduce Differentiable Programming System Bridging AI and Science | Yuqing Li - Synced - Medium] | * [http://medium.com/syncedreview/julia-computing-mit-introduce-differentiable-programming-system-bridging-ai-and-science-738c8a9eb0b9 Julia Computing & MIT Introduce Differentiable Programming System Bridging AI and Science | Yuqing Li - Synced - Medium] | ||
| + | * [http://fluxml.ai/Zygote.jl/dev/ Zygote] Zygote extends the Julia language to support differentiable programming. With Zygote you can write down any Julia code you feel like – including using existing Julia packages – then get gradients and optimise your program. Deep learning, ML and probabilistic programming are all different kinds of differentiable programming that you can do with Zygote. | ||
Differentiable programs are programs that rewrite themselves at least one component by optimizing along a gradient, like neural networks do using optimization algorithms such as gradient descent. Here’s a graphic illustrating the difference between differential and probabilistic programming approaches. [http://pathmind.com/wiki/differentiableprogramming A Beginner's Guide to Differentiable Programming | Chris Nicholson - A.I. Wiki pathmind] | Differentiable programs are programs that rewrite themselves at least one component by optimizing along a gradient, like neural networks do using optimization algorithms such as gradient descent. Here’s a graphic illustrating the difference between differential and probabilistic programming approaches. [http://pathmind.com/wiki/differentiableprogramming A Beginner's Guide to Differentiable Programming | Chris Nicholson - A.I. Wiki pathmind] | ||
Revision as of 17:29, 26 April 2020
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- Programming paradigms | Wikipedia
- Automated Machine Learning (AML) - AutoML
- Julia Computing & MIT Introduce Differentiable Programming System Bridging AI and Science | Yuqing Li - Synced - Medium
- Zygote Zygote extends the Julia language to support differentiable programming. With Zygote you can write down any Julia code you feel like – including using existing Julia packages – then get gradients and optimise your program. Deep learning, ML and probabilistic programming are all different kinds of differentiable programming that you can do with Zygote.
Differentiable programs are programs that rewrite themselves at least one component by optimizing along a gradient, like neural networks do using optimization algorithms such as gradient descent. Here’s a graphic illustrating the difference between differential and probabilistic programming approaches. A Beginner's Guide to Differentiable Programming | Chris Nicholson - A.I. Wiki pathmind
TensorFlow 1 uses the static graph approach, whereas TensorFlow 2 uses the dynamic graph approach by default. Differentiable programming | Wikipedia
