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CUDA vs scikit-learn: What are the differences?
CUDA: It provides everything you need to develop GPU-accelerated applications. A parallel computing platform and application programming interface model,it enables developers to speed up compute-intensive applications by harnessing the power of GPUs for the parallelizable part of the computation; scikit-learn: 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.
CUDA and scikit-learn can be primarily classified as "Machine Learning" tools.
scikit-learn is an open source tool with 36.5K GitHub stars and 17.9K GitHub forks. Here's a link to scikit-learn's open source repository on GitHub.
Repro, MonkeyLearn, and Home61 are some of the popular companies that use scikit-learn, whereas CUDA is used by Cruise, Replica Labs, and Enthusiasts First. scikit-learn has a broader approval, being mentioned in 104 company stacks & 252 developers stacks; compared to CUDA, which is listed in 13 company stacks and 13 developer stacks.
Pros of CUDA
Pros of scikit-learn
- Scientific computing25
- Easy19
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Cons of CUDA
Cons of scikit-learn
- Limited2
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What is CUDA?
What is scikit-learn?
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What companies use scikit-learn?
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