Differentiable Programming

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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