Keras vs. Kimola Cognitive

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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Since using the tool for 4 months we have been extremely pleased with its performance. I've decided to share this review after receiving an email from the Kimola Team, and once I'm in the consumer insights business, I'll definitely support them. The interface and design are fantastic, with a great choice of colors, and Kimola has consistently introduced numerous improvements to the product since we first started using it. The ease of use is unmatched, allowing us to gain new insights and perspectives that were previously unattainable

• 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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• Despite exploring various software options to analyze client feedback, none have proven as specific and accurate as Kimola Cognitive.

• 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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• I believe that more language support should be added and it should reach more customers.

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.

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.

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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• 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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• In order to create a custom model, if you are not experienced in this field, you need to watch a video on youtube. This question has little to do with Kimola.