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MXNet vs TensorFlow: 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; TensorFlow: Open Source Software Library for Machine Intelligence. 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.

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

MXNet is an open source tool with 17.5K GitHub stars and 6.21K GitHub forks. Here's a link to MXNet's open source repository on GitHub.

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Pros of MXNet
Pros of TensorFlow
  • 2
    User friendly
  • 29
    High Performance
  • 17
    Connect Research and Production
  • 14
    Deep Flexibility
  • 11
  • 10
    True Portability
  • 4
  • 4
    High level abstraction
  • 4
    Easy to use

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Cons of MXNet
Cons of TensorFlow
    Be the first to leave a con
    • 9
    • 6
      Hard to debug
    • 1
      Documentation not very helpful

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