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jax
Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
thinc - defining neural nets in functional way jax, a new deep learning framework puts emphasis on functions rather than tensors, I've tested it for a couple of applications and it's really cool, you can write stuff like you'd write math expressions in papers using numpy. That speeds up development significantly, and makes code much more readable
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InfluxDB
InfluxDB – Built for High-Performance Time Series Workloads. InfluxDB 3 OSS is now GA. Transform, enrich, and act on time series data directly in the database. Automate critical tasks and eliminate the need to move data externally. Download now.
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If you want to do FP style programming in Python you technically can, but it's been made to make the whole thing less pleasant and the language and community continues to reflect that. It would make more sense to try using something like Coconut, which is to Python what Scala is to Java: essentially a superset of the language that works in the same ecosystem. Or use an entirely different language like F# or OCaml, but that's a bigger jump than going from Python to Coconut.
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thinc - defining neural nets in functional way jax, a new deep learning framework puts emphasis on functions rather than tensors, I've tested it for a couple of applications and it's really cool, you can write stuff like you'd write math expressions in papers using numpy. That speeds up development significantly, and makes code much more readable
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Another examples: pfun - stuff you'd find in FP language, but in Python (like using monads for effects)
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