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Cortex.dev

7
19
+ 1
0
TensorFlow

3.8K
3.4K
+ 1
106
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TensorFlow vs Cortex.dev: What are the differences?

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; Cortex.dev: Deploy machine learning models in production. It is an open source platform that takes machine learning models—trained with nearly any framework—and turns them into production web APIs in one command.

TensorFlow and Cortex.dev can be categorized as "Machine Learning" tools.

TensorFlow and Cortex.dev are both open source tools. It seems that TensorFlow with 137K GitHub stars and 78.3K forks on GitHub has more adoption than Cortex.dev with 1.42K GitHub stars and 69 GitHub forks.

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Pros of Cortex.dev
Pros of TensorFlow
    Be the first to leave a pro
    • 32
      High Performance
    • 19
      Connect Research and Production
    • 16
      Deep Flexibility
    • 12
      Auto-Differentiation
    • 11
      True Portability
    • 6
      Easy to use
    • 5
      High level abstraction
    • 5
      Powerful

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    Cons of Cortex.dev
    Cons of TensorFlow
      Be the first to leave a con
      • 9
        Hard
      • 6
        Hard to debug
      • 2
        Documentation not very helpful

      Sign up to add or upvote consMake informed product decisions

      What is Cortex.dev?

      It is an open source platform that takes machine learning models—trained with nearly any framework—and turns them into production web APIs in one command.

      What is 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.

      Need advice about which tool to choose?Ask the StackShare community!

      What companies use Cortex.dev?
      What companies use TensorFlow?
        No companies found
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        What tools integrate with Cortex.dev?
        What tools integrate with TensorFlow?

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        What are some alternatives to Cortex.dev and TensorFlow?
        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.
        scikit-learn
        scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
        Keras
        Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
        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.
        Streamlit
        It is the app framework specifically for Machine Learning and Data Science teams. You can rapidly build the tools you need. Build apps in a dozen lines of Python with a simple API.
        See all alternatives