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MXNet

40
67
+ 1
1
PyTorch

1K
1.1K
+ 1
42
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MXNet vs PyTorch: What are the differences?

MXNet: 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; PyTorch: A deep learning framework that puts Python first. 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.

MXNet and PyTorch belong to "Machine Learning Tools" category of the tech stack.

MXNet and PyTorch are both open source tools. It seems that PyTorch with 30.5K GitHub stars and 7.46K forks on GitHub has more adoption than MXNet with 17.5K GitHub stars and 6.21K GitHub forks.

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Pros of MXNet
Pros of PyTorch
  • 1
    User friendly
  • 14
    Easy to use
  • 11
    Developer Friendly
  • 10
    Easy to debug
  • 7
    Sometimes faster than TensorFlow

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Cons of MXNet
Cons of PyTorch
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    • 3
      Lots of code

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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 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.

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

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    What are some alternatives to MXNet and PyTorch?
    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.
    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.
    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.
    See all alternatives