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For the parallelisation and the learning, using Jax is surely a good idea. fractal-jax employs the just in time (JIT) compilation to speed up things.
However, this library, as it's implemented now, does not operate any learning. The equation that generated the fractals is given, i.e., z = z^2 + c.
If I correctly understood your task, you could substitute the equation that generates the fractal with a parametric function and learn the parameter by optimising a loss.
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Thanks for answer! yes exactly - I do not expect for you to support the training per se I just need differentiable parametrized functions :). Maybe I am not very precise - generally, I am working on some models in Jax and already have an optimization loop, I had never worked with fractals but images of fractals seem quite similar to natural tissue images. Hence my question.
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