IBM Watson Natural Language Understanding vs. Keras

IBM Watson Natural Language Understanding is a Swiss Army knife that can be used in many scenarios. An extensive list of easy to use APIs is provided making it very easy to integrate it in any environment. The text analysis is decent and above market average. It generates results in many forms to suit may scenarios (important keywords, concepts, sentiment analysis, etc.).

Incentivized

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

• Easy to use and extensive APIs.
• Decent accuracy.
• It recognizes concepts and semantic roles.

Incentivized

• 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

• Improve Sentiment Analysis accuracy.
• Prevent having conflicting results (sad and happy, etc.).
• Foreign names detection.

Incentivized

• 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.

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

• Reduced development time.
• Increased solution efficiency in understanding the user.
• Increased solution scalability.

Incentivized

• 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