Best wrapper library for TensorFlow
Overall Satisfaction with Keras
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
- Good and easy way to develop neural network models
- Doesn't provide support for language other than Python
- Developed a natural language processing model that is quite easy and efficient
- TensorFlow and MATLAB
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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