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    ProductRatingMost Used ByProduct SummaryStarting Price

    Personiv

    Score7 out of 10
    N/APersoniv is an accounting outsourcing service, boasting expertise in managing financial details, managing invoicing, credit, and collections, procure to pay, financiali planning and analysis, and specialized accounting to meet any unique needs.N/A

    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
    Pricing
    PersonivTensorFlow
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    Pricing Offerings
    PersonivTensorFlow
    Free Trial
    NoNo
    Free/Freemium Version
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    Premium Consulting/Integration Services
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    Entry-level Setup FeeNo setup feeNo setup fee
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    PersonivTensorFlow
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    Google Cloud AI
    Score8.7 out of 10
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    Google Cloud AI
    Score8.7 out of 10
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    Google Cloud AI
    Score8.7 out of 10
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    User Ratings
    PersonivTensorFlow
    Likelihood to Recommend
    7.0
    (1 ratings)
    6.0
    (15 ratings)
    Usability
    -
    (0 ratings)
    9.0
    (1 ratings)
    Support Rating
    -
    (0 ratings)
    9.1
    (2 ratings)
    Implementation Rating
    -
    (0 ratings)
    8.0
    (1 ratings)
    User Testimonials
    PersonivTensorFlow
    Likelihood to Recommend
    eClerx
    Start by carving out recurring, rule based work that eats up your bandwidth: AP, allocations, month end schedules. Build SOPs for them, give them access to your tools and treat them like part of your team. That said don't offload judgement heavy tasks.
    Incentivized
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    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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    Pros
    eClerx
    • The team's work is pretty consistent.
    • Their responsiveness is incredible too, they adapt really fast.
    • Instant availability and ability to scale during crunch is amazing
    Incentivized
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    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
    eClerx
    • The onboarding process did require a lot of upfront hand holding
    • Time zone gaps sometimes
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    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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    Usability
    eClerx
    No answers on this topic
    Open Source
    Support of multiple components and ease of development.
    Incentivized
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    Support Rating
    eClerx
    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
    eClerx
    No answers on this topic
    Open Source
    Use of cloud for better execution power is recommended.
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    Alternatives Considered
    eClerx
    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
    eClerx
    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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