Geckoboard vs. Keras

Great value for the money. Excellent for smaller agencies with multiple projects and teams in a smaller space. We can quickly roll out mobile displays to help with a particular deployment push or monitoring a clients website engagement. It's also useful for showing live data without requiring analytics to run reports from a CRM, etc.

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Keras is quite perfect, if the aim is to build the standard Deep Learning model, and materialize it to serve the real business use case, while it is not suitable if the purpose is for research and a lot of non-standard try out and customization are required, in that case either directly goes to low level TensorFlow API or Pytorch

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• User self-help - the video tutorials and a robust publicly accessible knowledgebase.
• Geckoboard can aggregate data from a number of other apps into a singular dashboard.
• Sleek, dark UI.

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• One of the reason to use Keras is that it is easy to use. Implementing neural network is very easy in this, with just one line of code we can add one layer in the neural network with all it's configurations.
• It provides lot of inbuilt thing like cov2d, conv2D, maxPooling layers. So it makes fast development as you don't need to write everything on your own. It comes with lot of data processing libraries in it like one hot encoder which also makes your development easy and fast.
• It also provides functionality to develop models on mobile device.

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• A bit more flexibility in the number of columns would be nice.
• Ability to scale to mobile device (web).
• App capabilities are missing on Android.

• As it is a kind of wrapper library it won't allow you to modify everything of its backend
• Unlike other deep learning libraries, it lacks a pre-defined trained model to use
• Errors thrown are not always very useful for debugging. Sometimes it is difficult to know the root cause just with the logs

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My company did not renew due to changes in the organization.

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With a simple interface and available templates, creating basic dashboards is easy. Obviously depending on the data you want to visualize, there may be higher learning curves. That being said, they have a huge amount of integrations and extensible frameworks. If you are using anything made in the past ten years there is an API function or integration that can get it talking to the platform. As such, it's pretty easy to hit the main data points you want and get it on a cheap display in front of your team.

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I am giving this rating depending on my experience so far with Keras, I didn't face any issue far. I would like to recommend it to the new developers.

The support levels vary based on the level of plan that you have but that's to be expected. Virtually everything except the Enterprise plan has basic chat/email support. While they are responsive they are not going to be much assistance in helping you figure out API calls or implementing 3rd party integrations. That is to be expected and the support community can pretty much get you in the right direction if you look.

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Keras have really good support along with the strong community over the internet. So in case you stuck, It won't so hard to get out from it.

Domo is more robust. We do use both because Geckoboard is really easy to show the rest of our team without the need for additional licenses.

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Keras is good to develop deep learning models. As compared to TensorFlow, it's easy to write code in Keras. You have more power with TensorFlow but also have a high error rate because you have to configure everything by your own. And as compared to MATLAB, I will always prefer Keras as it is easy and powerful as well.

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• While we originally used this as an internal IS tool, we eventually have expanded it to be used by nearly every department.
• Because pricing is monthly, we can grow or decrease our usage based on our current client needs.
• Because it is low cost and easy to deploy, we can utilize it in place of considerable resources in analytics and reporting by delivering snapshots of data without pulling reports.

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• Easy and faster way to develop neural network.
• It would be much better if it is available in Java.
• It doesn't allow you to modify the internal things.

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