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Fritz

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4
+ 1
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H2O

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Fritz vs H2O: What are the differences?

Fritz: Mobile machine learning made easy. Fritz is the end-to-end solution for on-device ML. Create ML-powered features in your app with ease and cross-platform support; H2O: H2O.ai AI for Business Transformation. H2O.ai is the maker behind H2O, the leading open source machine learning platform for smarter applications and data products. H2O operationalizes data science by developing and deploying algorithms and models for R, Python and the Sparkling Water API for Spark.

Fritz and H2O belong to "Machine Learning Tools" category of the tech stack.

H2O is an open source tool with 4.15K GitHub stars and 1.51K GitHub forks. Here's a link to H2O's open source repository on GitHub.

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Pros of Fritz
Pros of H2O
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    • 2
      Highly customizable
    • 2
      Very fast and powerful
    • 2
      Auto ML is amazing
    • 2
      Super easy to use

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    Cons of Fritz
    Cons of H2O
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      • 1
        Not very popular

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      - No public GitHub repository available -

      What is Fritz?

      Fritz is the end-to-end solution for on-device ML. Create ML-powered features in your app with ease and cross-platform support.

      What is H2O?

      H2O.ai is the maker behind H2O, the leading open source machine learning platform for smarter applications and data products. H2O operationalizes data science by developing and deploying algorithms and models for R, Python and the Sparkling Water API for Spark.

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

      What companies use Fritz?
      What companies use H2O?
        No companies found
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        What tools integrate with Fritz?
        What tools integrate with H2O?
          No integrations found
          What are some alternatives to Fritz and H2O?
          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.
          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.
          See all alternatives