IntelliJ IDEA vs. PyCharm vs. TensorFlow

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.

Incentivized

PyCharm is well suited to developing and deploying Python applications in the cloud using Kubernetes or serverless pipelines. The integration with GitLab is great; merges and rebates are easily done and help the developer move quickly. The search engine that allows you to search inside your code is also great. It is less appropriate for other languages.

Incentivized

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

• 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.

Incentivized

• Git integration is really essential as it allows anyone to visually see the local and remote changes, compare revisions without the need for complex commands.
• Complex debugging tools are basked into the IDE. Controls like break on exception are sometimes very helpful to identify errors quickly.
• Multiple runtimes - Python, Flask, Django, Docker are native the to IDE. This makes development and debugging and even more seamless.
• Integrates with Jupyter and Markdown files as well. Side by side rendering and editing makes it simple to develop such files.

Incentivized

• 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

• Finding if a feature exists or not in IntelliJ IDEA can be challenging.
• For example, if we know how to format a particular file, the command is Ctrl+Alt+Shift+L, but if we don't, then finding it is difficult.
• Setting up a project interpreter and directory structure might not be intuitive at first.
• Git integration can be improved. For example, it isn't easy to rebase using UI in IntelliJ IDEA.

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• The biggest complaint I have about PyCharm is that it can use a lot of RAM which slows down the computer / IDE. I use the paid version, and have otherwise found nothing to complain about the interface, utility, and capabilities.

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• 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.

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.

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It's perfect for our needs, cuts development time, is really helpful for newbies to understand projects structure

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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.

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It's pretty easy to use, but if it's your first time using it, you need time to adapt. Nevertheless, it has a lot of options, and everything is pretty easy to find. The console has a lot of advantages and lets you accelerate your development from the first day.

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Support of multiple components and ease of development.

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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.

Incentivized

I rate 10/10 because I have never needed a direct customer support from the JetBrains so far. Whenever and for whatever kind of problems I came across, I have been able to resolve it within the internet community, simply by Googling because turns out most of the time, it was me who lacked the proper information to use the IDE or simply make the proper configuration. I have never came across a bug in PyCharm either so it deserves 10/10 for overall support

Incentivized

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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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.

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Use of cloud for better execution power is recommended.

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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.

Incentivized

When it comes to development and debugging PyCharm is better than Spyder as it provides good debugging support and top-quality code completion suggestions. Compared to Jupiter notebook it's easy to install required packages in PyCharm, also PyChram is a good option when we want to write production-grade code because it provides required suggestions.

Incentivized

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

• Easier to find bugs and debugs, thus reducing man hours and generating immediate dollar impact.
• Coding time is lessened, which in turn again reduces man hours and generates immediate dollar impact.
• Refactoring code is more innovative and easy here, resulting in more maintainable code.

Incentivized

• PyCharm has a very positive ROI for our BU. It has increased developer productivity exponentially.
• Software quality has significantly improved. We are able to refactor/test/debug the code quicker/faster/better.
• Our business unit is able to deliver faster. Customers are happier than ever.

Incentivized

• 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