IntelliJ IDEA is an IDE that aims to give Java and Kotlin developers everything they need out of the box, including a smart code editor, built-in developer tools, framework support, database support, web development support, and much more.
$19.90
per month
TensorFlow
Score 7.7 out of 10
N/A
TensorFlow is an open-source machine learning software library for numerical computation using data flow graphs. It was originally developed by Google.
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Pricing
IntelliJ IDEA
TensorFlow
Editions & Modules
For Individual Use (Monthly billing)
$19.90
per month
For Organizations (Monthly billing)
$71.90
per month
For Individual Use (Yearly billing)
$199
per year
For Organizations (Yearly billing)
$719
per year
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Offerings
Pricing Offerings
IntelliJ IDEA
TensorFlow
Free Trial
Yes
No
Free/Freemium Version
Yes
No
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
No setup fee
No setup fee
Additional Details
All Products Pack (For Individual Use) – $299 /1st year, $ 239 /2nd year and $ 179 /3d year onwards
All Products Pack (For Organizations) – $979 / year
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Community Pulse
IntelliJ IDEA
TensorFlow
Considered Both Products
IntelliJ IDEA
Verified User
Professional
Chose IntelliJ IDEA
Out of all three, IntellIj is the best. The other two are light weight but don't have the plugins or code quality that Intellij provides.
I have used Keras and MATLAB along with this. Also used Caffe and pyTorch sometimes, but all of them are not as powerful as TensorFlow. Keras is in good competition with TensorFlow but Keras won't allow you a lot of customization in your algorithms. And TensorFlow gives you the …
One major advantage of TensorFlow over Keras and other deep learning libraries is that it is the most powerful. It gives you power to write your own full customised algorithm that is not available in Keras. And it is fast too as compared to another tool as it can perform better …
This is a superb tool if your project involves a lot of backend development, especially in Java/Spring Boot and Kotlin. The support for the front end is great as well, but some developers may prefer to use the GitHub copilot add-on. I especially love using the GitHub copilot add-on. It may be less appropriate if your project requires heavy use of HotSwaps for backend debugging, as sometimes the support for that can be limited.
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).
Unit testing: Fully integrated into IntelliJ IDEA. Your unit tests will run smoothly and efficiently, with excellent debugging tools for when things get tricky.
Spring integration: Our Spring project using Maven works flawlessly in IntelliJ IDEA. I know firsthand that Apache is also easily and readily supported too. The integration is seamless and very easy to set up using IntelliJ IDEA's set up wizard when importing new projects.
Customization: IntelliJ IDEA comes out of the box with a bunch of handy shortcuts, as well as text prediction, syntax error detection, and other tools to help keep your code clean. But even better is that it allows for total customization of shortcuts you can easily create to suit your needs.
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.
VS Code is maturing and has a Scala plugin now. The overall experience with VS Code - for web development at least - is very snappy/fast. IntelliJ feels a bit sluggish in comparison. If that Scala plugin for VS Code is deemed mature enough - we may not bother renewing and resort to the Community Edition if we need it.
There is always room for improvement, but I haven't met any IDE that I liked more so far. Even if it did not fit a use case right out of the box, there is always a way to configure how it works to do just that.
Customer support is really good in the case of IntelliJ. If you are paying for this product then, the company makes sure that you will get all the services adequately. Regular update patches are provided to improve the IDE. An online bug report makes it easier for the developers to find the solution as fast as possible. The large online community also helps to find the various solutions to the issues.
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.
This installs just like any other application - its pretty straight forward. Perhaps licensing could be more challenging - but if you use the cloud licensing they offer its as simple as having engineers login to the application and it just works.
Eclipse is just so old, like a dinosaur, compared to IntelliJ. There are still formats that Eclipse supports better, especially old and/or propriety ones. Still, most of the modern software development needs can be done on IntelliJ, & in a much better way, some of them are not even supported on Eclipse.
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
