Difference between revisions of "Differentiable Programming"
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* [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. | * [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. | ||
| − | “People are now building a new kind of software by assembling networks of parameterized functional blocks and by training them from examples using some form of gradient-based optimization.” - Facebook Chief AI Scientist Yann LeCun | + | “People are now building a new kind of software by assembling networks of parameterized functional blocks and by training them from examples using some form of gradient-based optimization.” - [[Meta|Facebook]] Chief AI Scientist [[Creatives#Yann LeCun | Yann LeCun]] |
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 23:14, 8 February 2023
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- Programming paradigms | Wikipedia
- Automated Learning
- Automatic differentiation - Wikipedia
- Graph
- 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.
“People are now building a new kind of software by assembling networks of parameterized functional blocks and by training them from examples using some form of gradient-based optimization.” - Facebook Chief AI Scientist Yann LeCun
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
