Julia thread

This is 100% #JuliaLang code. It trains a dense neural network using the #Keras Python library. If you replace the first 3 lines with "import tensorflow as tf; from tensorflow import keras", you can run the exact same code in Python. Talk about excellent interoperability!
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5:19 AM · Jun 30, 2020Twitter Web App
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Such an interesting call! I mean, how useful is to try this tool enhancing the speed by using Julia. I would like to know if JuliaML.jl gives or not a better result than you presented?
Good question! This example just shows that you can use any Python library in Julia. This makes it easier to transition from Python to Julia.
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Pure Julia code is 10x to 100x faster than pure Python code, in most cases. It can even be faster than Python C modules, like NumPy, in some cases. For example sin.(cos.(exp.(A))) in Julia will iterate over the array A just once, whereas NumPy would create 2 temporary arrays.
I haven't run any benchmarks on this, but I assume that everything that interacts with Python runs at Python speed, plus a small overhead due to the conversion. But that overhead is small as Python runs in the Julia process itself. The benefit is interoperability, not speed.
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Yes, you could do that. However, in the long term Julia solves the two-language problem, where you need one language for high level stuff and another for low level. With Julia, you can write high level code and it will run fast.

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