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CuPy

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jQuery

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Pros of CuPy
Pros of jQuery
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    • 1.3K
      Cross-browser
    • 957
      Dom manipulation
    • 809
      Power
    • 660
      Open source
    • 610
      Plugins
    • 459
      Easy
    • 395
      Popular
    • 350
      Feature-rich
    • 281
      Html5
    • 227
      Light weight
    • 93
      Simple
    • 84
      Great community
    • 79
      CSS3 Compliant
    • 69
      Mobile friendly
    • 67
      Fast
    • 43
      Intuitive
    • 42
      Swiss Army knife for webdev
    • 35
      Huge Community
    • 11
      Easy to learn
    • 4
      Clean code
    • 3
      Because of Ajax request :)
    • 2
      Powerful
    • 2
      Nice
    • 2
      Just awesome
    • 2
      Used everywhere
    • 1
      Improves productivity
    • 1
      Javascript
    • 1
      Easy Setup
    • 1
      Open Source, Simple, Easy Setup
    • 1
      It Just Works
    • 1
      Industry acceptance
    • 1
      Allows great manipulation of HTML and CSS
    • 1
      Widely Used
    • 1
      I love jQuery

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    Cons of CuPy
    Cons of jQuery
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      • 6
        Large size
      • 5
        Sometimes inconsistent API
      • 5
        Encourages DOM as primary data source
      • 2
        Live events is overly complex feature

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      What is CuPy?

      It is an open-source matrix library accelerated with NVIDIA CUDA. CuPy provides GPU accelerated computing with Python. It uses CUDA-related libraries including cuBLAS, cuDNN, cuRand, cuSolver, cuSPARSE, cuFFT and NCCL to make full use of the GPU architecture.

      What is jQuery?

      jQuery is a cross-platform JavaScript library designed to simplify the client-side scripting of HTML.

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      What companies use CuPy?
      What companies use jQuery?
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        What tools integrate with CuPy?
        What tools integrate with jQuery?

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        What are some alternatives to CuPy and jQuery?
        NumPy
        Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.
        Numba
        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.
        PyTorch
        PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.
        CUDA
        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.
        TensorFlow
        TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
        See all alternatives