Sage X3 vs. TensorFlow

I wouldn't suggest this ERP for small organizations, especially if there is no IT staff. While they do offer support packages, they can be costly as certain packages limit the number of times you can call in. Hiring an outside consulting firm to support is another route, however that could become costly as well. Medium to large businesses with savvy tech staff would be a good fit for this ERP.

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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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• Easy to understand software for new users.
• Journal entry and review process is easy.
• Sage is efficient in tracking multi subsidiary transactions.

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• 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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• The reports
• Imports

• 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.

The interface could be a little bit more friendlier and intuitive. Normally end users complain about the ergonomics of the system, even though my experience tells me that is a situation that dilutes itself pretty fast. Another aspect that I think Sage could improve, is the way reports and queries are managed, it seems that at least at some level, there could be a easy drag and drop way (or just by picking fields) to create simple queries/reports. Having to rely on some data base knowledge is not very user friendly. But overall the system is very powerful and a good tool for companies that want to have control over their operations.

Support of multiple components and ease of development.

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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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Use of cloud for better execution power is recommended.

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Unfortunately, the product was purchased before I began working at the organization. I am unsure what other products they may have reviewed before purchasing Sage ERP X3.

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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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• Sage was easy to setup when we got started.
• Exporting data out of sage has been extremely hard.
• Sage is quite fairly priced compared to other alternatives.

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• 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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