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    Overview
    ProductRatingMost Used ByProduct SummaryStarting Price

    Oracle Java SE

    Score8.1 out of 10
    N/AOracle Java SE is a programming language and gives customers enterprise features that minimize the costs of deployment and maintenance of their Java-based IT environment.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
    Oracle Java SETensorFlow
    Editions & Modules
    No answers on this topic
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    Offerings
    Pricing Offerings
    Oracle Java SETensorFlow
    Free Trial
    NoNo
    Free/Freemium Version
    NoNo
    Premium Consulting/Integration Services
    NoNo
    Entry-level Setup FeeNo setup feeNo setup fee
    Additional Details——
    More Pricing Information
    Community Pulse
    Oracle Java SETensorFlow
    Considered Both Products
    Oracle
    No answer on this topic
    Open Source
    No answer on this topic
    Key User Insights
    Would buy again
    96%
    Would buy again
    23 Answers
    No answers on this topic
    Delivers good value for the price
    94%
    Delivers good value for the price
    16 Answers
    No answers on this topic
    Happy with the feature set
    100%
    Happy with the feature set
    24 Answers
    No answers on this topic
    Lived up to sales and marketing promises
    100%
    Lived up to sales and marketing promises
    13 Answers
    No answers on this topic
    Implementation went as expected
    100%
    Implementation went as expected
    22 Answers
    No answers on this topic
    Best Alternatives
    Oracle Java SETensorFlow
    Small Businesses
    GraalVM
    Score9.1 out of 10
    Google Cloud AI
    Score8.7 out of 10
    Medium-sized Companies
    Java Cloud
    Score8.8 out of 10
    Google Cloud AI
    Score8.7 out of 10
    Enterprises
    GraalVM
    Score9.1 out of 10
    Google Cloud AI
    Score8.7 out of 10
    All AlternativesView all alternativesView all alternatives
    User Ratings
    Oracle Java SETensorFlow
    Likelihood to Recommend
    9.0
    (33 ratings)
    6.0
    (15 ratings)
    Usability
    9.0
    (3 ratings)
    9.0
    (1 ratings)
    Support Rating
    8.0
    (19 ratings)
    9.1
    (2 ratings)
    Implementation Rating
    -
    (0 ratings)
    8.0
    (1 ratings)
    User Testimonials
    Oracle Java SETensorFlow
    Likelihood to Recommend
    Oracle
    Oracle Java SE is well suited to long-running applications (e.g. servers). Java Swing (UI toolkit) is now rather outdated, lacking support for modern UI features. JavaFX, the potential replacement for Swing, has now been separated out of Java core. Ideally, there would be a path to migrate a large application incrementally from Swing to JavaFX, but due to different threading models and other aspects, it is difficult. At this point, it is probably better to use an embedded web browser (e.g. JxBrowser) to provide a modern UI in HTML/Javascript and keep just the business logic in Java.
    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).
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    Pros
    Oracle
    • Plenty support built into the tool and IDE like Maven, Ant, Eclipse, IntelliJ.
    • Strong object-orientation language and clear project structure.
    • Wrapper underlines hardware and memory management so the developers can focus on business and implementation.
    • It offers a huge library and framework support from third-parties and the community.
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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.
    Incentivized
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    Cons
    Oracle
    • Commercial Licensing in 2019. Oracle will charge commercial organizations using Java SE for upgrading to the latest bug fixes and updates. Organizations will now need to either limit their implementation of Java SE or may need to drop it altogether.
    • Slow Performance. Due to the all of the abstraction of the JVM, Java SE programs take much more resources to compile and run compared to Python.
    • Poor UI appearance on all of the major GUI libraries (Swing, SWT, etc.). Through Android Studio, it is easy to get a native look/feel for Java apps, but when it comes to desktops, the UI is far from acceptable (does not mimic the native OS's look/feel at all).
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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
    Oracle
    Oracle Java SE provides the new features along with timely security patches. New features like Record patterns and pattern matching for switches are very useful. With every new release of Java, it is getting better. Sequenced collections are also an interesting feature added to Java. With all these new features, backward compatibility is also maintained.
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    Open Source
    Support of multiple components and ease of development.
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    Support Rating
    Oracle
    Java is such a mature product at this point that there is little support from the vendor that is needed. Various sources on the internet, and especially StackOverflow, provide a wealth of knowledge and advice. Areas that may benefit from support is when dealing with complex multithreading issues and security libraries.
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    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
    Oracle
    No answers on this topic
    Open Source
    Use of cloud for better execution power is recommended.
    Incentivized
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    Alternatives Considered
    Oracle
    Chose to go with Java instead of Python or C++ due to the expertise on the ground with the technology, for its ease of integration with our heterogeneous setup of production servers, and for the third party library support which we've found was able to address some challenging aspects of our business problem.
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    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
    Oracle
    • The different versions make it harder to work with other companies where some use newer versions while some use older versions, costing time to make them compatible.
    • Licenses are getting to be costly, forcing us to consider OpenJDK as an alternative.
    • New features take time to learn. When someone starts using them, everyone has to take time to learn.
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    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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