Eclipse is a free and open source integrated development environment (IDE).
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TensorFlow
Score 7.7 out of 10
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TensorFlow is an open-source machine learning software library for numerical computation using data flow graphs. It was originally developed by Google.
I think that if someone asked me for an IDE for Java programming, I would definitely recommend Eclipse as is one of the most complete solutions for this language out there. If the main programming language of that person is not Java, I don't think Eclipse would suit his needs[.]
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).
While the DB integration is broad (many connectors) it isn't particularly deep. So if you need to do serious DB work on (for example) SQL Server, it is sometimes necessary to go directly to the SQL Server Studio. But for general access and manipulation, it is ok.
The syntax formatting is sometimes painful to set up and doesn't always support things well. For example, it doesn't effectively support SCSS.
Using it for remote debugging in a VM works pretty well, but it is difficult to set up and there is no documentation I could find to really explain how to do it. When remote debugging, the editor does not necessarily integrate the remote context. So, for example, things like Pylint don't always find the libraries in the VM and display spurious errors.
The debugging console is not the default, and my choice is never remembered, so every time I restart my program, it's a dialog and several clicks to get it back. The debugging console has the same contextual problems with remote debugging that the editor does.
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.
I love this product, what makes it one of the best tool out in the market is its ability to function with a wide range of languages. The online community support is superb, so you are never stuck on an issue. The customization is endless, you can keep adding plugins or jars for more functionalities as per your requirements. It's Free !!!
It has everything that the developer needs to do the job. Few things that I have used in my day-to-day development 1. Console output. 2. Software flash functionality supporting multiple JTAG vendors like J-LINK. 3. Debugging capabilities like having a breakpoint, looking at the assembly, looking at the memory etc. this also applies to Embedded boards. 4. Plug-in like CMake, Doxygen and PlantUML are available.
I gave this rating because Eclipse is an open-source free IDE therefore no support system is available as far as I know. I have to go through other sources to solve my problem which is very tough and annoying. So if you are using Eclipse then you are on your own, as a student, it is not a big issue for me but for developers it is a need.
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.
The installation, adaptability, and ease of usage for Eclipse are pretty high and simple compared to some of the other products. Also, the fact that it is almost a plug and play once the connections are established and once a new user gets the hang of the system comes pretty handy.
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
This development environment offers the possibility of improving the productivity time of work teams by supporting the integration of large architectures.
It drives constant change and evolution in work teams thanks to its constant versioning.
It works well enough to develop continuous server client integrations, based on solid or any other programming principle.
