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scikit-learn vs Numba: What are the differences?
Developers describe scikit-learn as "Easy-to-use and general-purpose machine learning in Python". scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license. On the other hand, Numba is detailed as "An open source JIT compiler that translates a subset of Python and NumPy code into fast machine code". It translates Python functions to optimized machine code at runtime using the industry-standard LLVM compiler library. It offers a range of options for parallelising Python code for CPUs and GPUs, often with only minor code changes.
scikit-learn and Numba can be categorized as "Machine Learning" tools.
scikit-learn is an open source tool with 36.8K GitHub stars and 18.1K GitHub forks. Here's a link to scikit-learn's open source repository on GitHub.
Pros of Numba
Pros of scikit-learn
- Scientific computing25
- Easy19
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Cons of Numba
Cons of scikit-learn
- Limited2
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What is Numba?
What is scikit-learn?
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What companies use Numba?
What companies use scikit-learn?
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What tools integrate with Numba?
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