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.

I would recommend it for use when anyone wants to quickly develop a neural network. Or if a user is solving any machine learning problem that includes deep learning. And this kind of problem will be like image recognition, face recognition, doing some text analysis using deep learning which includes LSTM or some other algorithm.

Incentivized

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

• Implementing neural networks and deep learning models is easy with this.
• Data processing is easy with Python and Keras. Keras helps a lot and has a good collection of functions to do data processing.
• It has good integration with other devices like Android.

Incentivized

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

• I didn't face any issue so far.
• The only thing, you can't modify everything in this. So it's not recommended for constructing highly optimised algorithms.

The reason for giving this much rating. 1. It makes my job really easy and fast. 2. Strong community support. 3. Overall cost.

Incentivized

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.

As Keras is the high level API, so using Keras, we don't have to be bothered by the low level TensorFlow complexity, and we can reduce a lot coding and testing efforts.

Incentivized

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

• It helped me in learning the basic concept of deep learning by having hands-on experience.
• It has helped us to implement our NN with very little time.
• It doesn't give you the whole power to customize your neural network. If you want that then you have to shift to TensorFLow

Incentivized