numericaal

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Polyaxon

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numericaal vs Polyaxon: What are the differences?

What is numericaal? Machine learning for mobile & IoT made easy. numericaal automates model optimization and management so you can focus on data and training.

What is Polyaxon? An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications. An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications.

numericaal and Polyaxon can be primarily classified as "Machine Learning" tools.

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What is numericaal?

numericaal automates model optimization and management so you can focus on data and training.

What is Polyaxon?

An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications.
What companies use numericaal?
What companies use Polyaxon?
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    What tools integrate with numericaal?
    What tools integrate with Polyaxon?
      No integrations found

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      What are some alternatives to numericaal and Polyaxon?
      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/
      scikit-learn
      scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
      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.
      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.
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