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    Overview
    ProductRatingMost Used ByProduct SummaryStarting Price

    TensorFlow

    Score7.6 out of 10
    N/ATensorFlow is an open-source machine learning software library for numerical computation using data flow graphs. It was originally developed by Google.N/A

    Tettra

    Score10 out of 10
    Small Businesses (1-50 employees)
    Tettra helps teams that use Slack organize and share important knowledge in one central, searchable, manageable place.N/A
    Pricing
    TensorFlowTettra
    Editions & Modules
    No answers on this topic
    No answers on this topic
    Offerings
    Pricing Offerings
    TensorFlowTettra
    Free Trial
    NoYes
    Free/Freemium Version
    NoNo
    Premium Consulting/Integration Services
    NoNo
    Entry-level Setup FeeNo setup feeNo setup fee
    Additional Details—Active users are people who sign up for Tettra with their Slack account
    More Pricing Information
    Best Alternatives
    TensorFlowTettra
    Small Businesses
    Google Cloud AI
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    Medium-sized Companies
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    Enterprises
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    User Ratings
    TensorFlowTettra
    Likelihood to Recommend
    6.0
    (15 ratings)
    8.0
    (3 ratings)
    Usability
    9.0
    (1 ratings)
    -
    (0 ratings)
    Support Rating
    9.1
    (2 ratings)
    8.0
    (1 ratings)
    Implementation Rating
    8.0
    (1 ratings)
    -
    (0 ratings)
    User Testimonials
    TensorFlowTettra
    Likelihood to Recommend
    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).
    Incentivized
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    Tettra, Inc.
    Tettra is excellent for a small team wiki. It is easy to set-up and use, and for many teams the free version is enough. New features are added regularly. One of the features I would like to see is 2FA for email accounts or even SAML integration.
    Incentivized
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    Pros
    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.
    Incentivized
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    Tettra, Inc.
    • Easily access revision history
    • Suggest pages to create
    • Find information
    Incentivized
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    Cons
    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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    Tettra, Inc.
    • More integrations
    • More options for presentation
    • More customization options
    Incentivized
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    Usability
    Open Source
    Support of multiple components and ease of development.
    Incentivized
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    Tettra, Inc.
    No answers on this topic
    Support Rating
    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.
    Incentivized
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    Tettra, Inc.
    I base the rating on the online availability of the help system. The product is quite intuitive but the questions I did have were answered on the online help. That is always better than having to contact support as then you have to wait longer than just a search on the website.
    Incentivized
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    Implementation Rating
    Open Source
    Use of cloud for better execution power is recommended.
    Incentivized
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    Tettra, Inc.
    No answers on this topic
    Alternatives Considered
    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
    Incentivized
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    Tettra, Inc.
    Tettra provides an easy interface, but feels limited in its feauture set. When I need to incorporate more granular permissions or live collaboration I use Google Drive.
    Incentivized
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    Return on Investment
    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.
    Incentivized
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    Tettra, Inc.
    • Easier to find information
    • Meets you on the platforms you already use - ie Slack and Google.
    Incentivized
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