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CUDA vs ScalaNLP: 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; ScalaNLP: A suite of machine learning and numerical computing libraries. ScalaNLP is a suite of machine learning and numerical computing libraries.

CUDA and ScalaNLP belong to "Machine Learning Tools" category of the tech stack.

ScalaNLP is an open source tool with 2.93K GitHub stars and 674 GitHub forks. Here's a link to ScalaNLP's open source repository on GitHub.

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What is 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.

What is ScalaNLP?

ScalaNLP is a suite of machine learning and numerical computing libraries.

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    What tools integrate with CUDA?
    What tools integrate with ScalaNLP?
    What are some alternatives to CUDA and ScalaNLP?
    OpenCL
    It is the open, royalty-free standard for cross-platform, parallel programming of diverse processors found in personal computers, servers, mobile devices and embedded platforms. It greatly improves the speed and responsiveness of a wide spectrum of applications in numerous market categories including gaming and entertainment titles, scientific and medical software, professional creative tools, vision processing, and neural network training and inferencing.
    OpenGL
    It is a cross-language, cross-platform application programming interface for rendering 2D and 3D vector graphics. The API is typically used to interact with a graphics processing unit, to achieve hardware-accelerated rendering.
    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.
    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.
    Keras
    Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
    See all alternatives