Best wrapper library for TensorFlow
Rating: 9 out of 10
IncentivizedUse Cases and Deployment Scope
Keras is being used to develop data science models for predictions that include implementing neural networks and others as well. It is not being used by all of us in our company but only by the data science team. We have used this not only for prediction, but for building NLP models as well. We have used this to implement LSTM. Basically, we use this to understand the natural language and to process that.
Pros
- 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.
Cons
- With Keras you don't have much power to configure your model. So, if it can be possible to do the customization to the deep level, then it will be good.
- It is only available for Python, doesn't have other language support.
- Would love to see dynamic chart creation, like PyTorch
Likelihood to Recommend
Scenarios where it is well suited include implementing deep learning algorithms. It is also good for natural language processing. It has some in built functions that are very useful for developing deep learning models. To build basic machine learning algorithms, which includes clustering and PCM, it may not be as good.
RP
Raghuvar Prajapati
Data Scientist in Information Technology at Pensieve (Computer Software, 11-50 employees)
Vetted Review
2 years of experience