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43 Ratings
This review does not include a rating.
43 Ratings
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Score 7.7 out of 100

Attribute Ratings

    Likelihood to Recommend

    Cloudera Altus (discontinued)

    N/A
    0 Ratings
    7.3

    TensorFlow

    73%
    14 Ratings

    Usability

    Cloudera Altus (discontinued)

    N/A
    0 Ratings
    9.0

    TensorFlow

    90%
    1 Rating

    Support Rating

    Cloudera Altus (discontinued)

    N/A
    0 Ratings
    9.1

    TensorFlow

    91%
    4 Ratings

    Implementation Rating

    Cloudera Altus (discontinued)

    N/A
    0 Ratings
    8.0

    TensorFlow

    80%
    2 Ratings

    Likelihood to Recommend

    Cloudera

    No answers on this topic

    Open Source

    TensorFlow is great for most deep learning purposes. This is especially true in two domains: 1. Computer vision: image classification, object detection and image generation via generative adversarial networks 2. Natural language processing: text classification and generation. The good community support often means that a lot of off-the-shelf models can be used to prove a concept or test an idea quickly. That, and Google's promotion of Colab means that ideas can be shared quite freely. Training, visualizing and debugging models is very easy in TensorFlow, compared to other platforms (especially the good old Caffe days). In terms of productionizing, it's a bit of a mixed bag. In our case, most of our feature building is performed via Apache Spark. This means having to convert Parquet (columnar optimized) files to a TensorFlow friendly format i.e., protobufs. The lack of good JVM bindings mean that our projects end up being a mix of Python and Scala. This makes it hard to reuse some of the tooling and support we wrote in Scala. This is where MXNet shines better (though its Scala API could do with more work).
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    Pros

    Cloudera

    No answers on this topic

    Open Source

    • A vast library of functions for all kinds of tasks - Text, Images, Tabular, Video etc.
    • Amazing community helps developers obtain knowledge faster and get unblocked in this active development space.
    • Integration of high-level libraries like Keras and Estimators make it really simple for a beginner to get started with neural network based models.
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    Cons

    Cloudera

    No answers on this topic

    Open Source

    • RNNs are still a bit lacking, compared to Theano.
    • Cannot handle sequence inputs
    • Theano is perhaps a bit faster and eats up less memory than TensorFlow on a given GPU, perhaps due to element-wise ops. Tensorflow wins for multi-GPU and “compilation” time.
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    Pricing Details

    Cloudera Altus (discontinued)

    Starting Price

    Editions & Modules

    Cloudera Altus (discontinued) editions and modules pricing
    EditionModules

    Footnotes

      Offerings

      Free Trial
      Free/Freemium Version
      Premium Consulting/Integration Services

      Entry-level set up fee?

      No setup fee

      Additional Details

      TensorFlow

      Starting Price

      Editions & Modules

      TensorFlow editions and modules pricing
      EditionModules

      Footnotes

        Offerings

        Free Trial
        Free/Freemium Version
        Premium Consulting/Integration Services

        Entry-level set up fee?

        No setup fee

        Additional Details

        Usability

        Cloudera

        No answers on this topic

        Open Source

        Support of multiple components and ease of development.
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        Support Rating

        Cloudera

        No answers on this topic

        Open Source

        Community support for TensorFlow is great. There's a huge community that truly loves the platform and there are many examples of development in TensorFlow. Often, when a new good technique is published, there will be a TensorFlow implementation not long after. This makes it quick to ally the latest techniques from academia straight to production-grade systems. Tooling around TensorFlow is also good. TensorBoard has been such a useful tool, I can't imagine how hard it would be to debug a deep neural network gone wrong without TensorBoard.
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        Implementation Rating

        Cloudera

        No answers on this topic

        Open Source

        Use of cloud for better execution power is recommended.
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        Alternatives Considered

        Cloudera

        No answers on this topic

        Open Source

        Keras is built on top of TensorFlow, but it is much simpler to use and more Python style friendly, so if you don't want to focus on too many details or control and not focus on some advanced features, Keras is one of the best options, but as far as if you want to dig into more, for sure TensorFlow is the right choice
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        Return on Investment

        Cloudera

        No answers on this topic

        Open Source

        • Learning is s bit difficult takes lot of time.
        • Developing or implementing the whole neural network is time consuming with this, as you have to write everything.
        • Once you have learned this, it make your job very easy of getting the good result.
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