Differentiable Programming - Revision history

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Text replacement - "http:" to "https:"

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BPeat at 00:07, 28 September 2020

2020-09-28T00:07:56Z

BPeat at 21:57, 26 April 2020

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@@ Line 5: / Line 5: @@
 |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 }}
-[http://www.youtube.com/results?search_query=Differentiable+Programming YouTube search...]
+[https://www.youtube.com/results?search_query=Differentiable+Programming YouTube search...]
-[http://www.google.com/search?q=Differentiable+Programming ...Google search]
+[https://www.google.com/search?q=Differentiable+Programming ...Google search]
-* [http://en.wikipedia.org/wiki/Category:Programming_paradigms Programming paradigms | Wikipedia]
+* [https://en.wikipedia.org/wiki/Category:Programming_paradigms Programming paradigms | Wikipedia]
 * [[Algorithm Administration#Automated Learning|Automated Learning]]
-* [http://en.wikipedia.org/wiki/Automatic_differentiation Automatic differentiation - Wikipedia]
+* [https://en.wikipedia.org/wiki/Automatic_differentiation Automatic differentiation - Wikipedia]
 * [[Graph]]
 ** [[Graph Convolutional Network (GCN), Graph Neural Networks (Graph Nets), Geometric Deep Learning]]
-* [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]
+* [https://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.
+* [https://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.” - [[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. [https://pathmind.com/wiki/differentiableprogramming 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. [http://en.wikipedia.org/wiki/Differentiable_programming Differentiable programming | Wikipedia]
+TensorFlow 1 uses the static graph approach, whereas TensorFlow 2 uses the dynamic graph approach by default. [https://en.wikipedia.org/wiki/Differentiable_programming Differentiable programming | Wikipedia]
-<img src="http://pathmind.com/images/wiki/differentiable_probabilistic.jpg" width="800" height="500">
+<img src="https://pathmind.com/images/wiki/differentiable_probabilistic.jpg" width="800" height="500">
 <youtube>qhPBfysSYI8</youtube>

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 |title=PRIMO.ai
 |titlemode=append
-|keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Google, Nvidia, Microsoft, Azure, Amazon, AWS
+|keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Facebook, Meta, Google, Nvidia, Microsoft, Azure, Amazon, AWS
 |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 }}
@@ Line 16: / Line 16: @@
 * [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]

@@ Line 9: / Line 9: @@
 * [http://en.wikipedia.org/wiki/Category:Programming_paradigms Programming paradigms | Wikipedia]
-* [[Automated Machine Learning (AML) - AutoML]]
+* [[Algorithm Administration#Automated Learning|Automated Learning]]
 * [http://en.wikipedia.org/wiki/Automatic_differentiation Automatic differentiation - Wikipedia]
 * [[Graph]]

@@ Line 10: / Line 10: @@
 * [http://en.wikipedia.org/wiki/Category:Programming_paradigms Programming paradigms | Wikipedia]
 * [[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://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.

@@ Line 12: / Line 12: @@
 * [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]

← Older revision		Revision as of 21:24, 26 April 2020
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	<img src="http://pathmind.com/images/wiki/differentiable_probabilistic.jpg" width="800" height="500">		<img src="http://pathmind.com/images/wiki/differentiable_probabilistic.jpg" width="800" height="500">
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		+	<youtube>qhPBfysSYI8</youtube>
		+	<youtube>lk0PhtSHE38</youtube>
		+	<youtube>5NeqdfUPUm4</youtube>
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← Older revision		Revision as of 21:20, 26 April 2020
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	TensorFlow 1 uses the static graph approach, whereas TensorFlow 2 uses the dynamic graph approach by default. [http://en.wikipedia.org/wiki/Differentiable_programming Differentiable programming \| Wikipedia]		TensorFlow 1 uses the static graph approach, whereas TensorFlow 2 uses the dynamic graph approach by default. [http://en.wikipedia.org/wiki/Differentiable_programming Differentiable programming \| Wikipedia]

−	<img src="http://pathmind.com/images/wiki/differentiable_probabilistic.jpg" width="800" height="~~700~~">	+	<img src="http://pathmind.com/images/wiki/differentiable_probabilistic.jpg" width="800" height="500">

← Older revision		Revision as of 21:19, 26 April 2020
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	TensorFlow 1 uses the static graph approach, whereas TensorFlow 2 uses the dynamic graph approach by default. [http://en.wikipedia.org/wiki/Differentiable_programming Differentiable programming \| Wikipedia]		TensorFlow 1 uses the static graph approach, whereas TensorFlow 2 uses the dynamic graph approach by default. [http://en.wikipedia.org/wiki/Differentiable_programming Differentiable programming \| Wikipedia]

−	http://pathmind.com/images/wiki/differentiable_probabilistic.jpg	+	<img src="http://pathmind.com/images/wiki/differentiable_probabilistic.jpg" width="800" height="700">