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MXNet

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ScalaNLP

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0
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MXNet vs ScalaNLP: What are the differences?

Developers describe MXNet as "A flexible and efficient library for deep learning". A deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, it contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly. On the other hand, ScalaNLP is detailed as "A suite of machine learning and numerical computing libraries". ScalaNLP is a suite of machine learning and numerical computing libraries.

MXNet and ScalaNLP can be categorized as "Machine Learning" tools.

Some of the features offered by MXNet are:

  • Lightweight
  • Portable
  • Flexible distributed/Mobile deep learning

On the other hand, ScalaNLP provides the following key features:

  • ScalaNLP is the umbrella project for several libraries:
  • Breeze is a set of libraries for machine learning and numerical computing
  • Epic is a high-performance statistical parser and structured prediction library

MXNet and ScalaNLP are both open source tools. MXNet with 17.5K GitHub stars and 6.21K forks on GitHub appears to be more popular than ScalaNLP with 2.93K GitHub stars and 674 GitHub forks.

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    What is MXNet?

    A deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, it contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly.

    What is ScalaNLP?

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

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    What companies use MXNet?
    What companies use ScalaNLP?
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      What tools integrate with MXNet?
      What tools integrate with ScalaNLP?

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      What are some alternatives to MXNet and ScalaNLP?
      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/
      Theano
      Theano is a Python library that lets you to define, optimize, and evaluate mathematical expressions, especially ones with multi-dimensional arrays (numpy.ndarray).
      Gluon
      A new open source deep learning interface which allows developers to more easily and quickly build machine learning models, without compromising performance. Gluon provides a clear, concise API for defining machine learning models using a collection of pre-built, optimized neural network components.
      See all alternatives