Appen vs. Keras

It is well suited for the users and potential employee who are free of any job perspective and need their free time to be utilized. Users can use their free time to be used for submission of interesting tasks.

Whereas the number of tasks are very less and processing time is also very extensive and recruitment takes time more.

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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• Project listing
• Hiring of the potential and qualified users
• Tracking of the projects

• 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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• Selection procedure is bit .
• The questionnaire need to be reviewed.

• 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 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.

Appen offers projects mostly related to my native language and also according to my expertise . It offers very interesting projects to be completed , which requires not very expertise and less time to be completed for each task. It is also very convenient to use after selection for the task and also well rewarding against the time consumed for the task completion.

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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• It has Positive impact as it provides opportunity for new jobs in my area of expertise.

• 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.

Incentivized

Independently Invited by TrustRadius

A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info