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Python, OCaml, and Machine Learning with Laurent Mazare
Signals and Threads
Using Swift as a Language for Machine Learning
The idea is indeed a differentiable programming. So what you tend to do a lot in modern machine learning is doing gradient descent. And having the ability to compute the derivative there is very, a very powerful thing. If you have a model that has millions of parameters, you don't want to compute the gradient numerically. That's where a differential programming is actually very helpful.
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