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https://dudodiprj2sv7.cloudfront.net/product-logos/ng/gJ/BNTOBVI492DS.PNGBest wrapper library for TensorFlow and Theano.Keras is being used during hackathon in my current company. And it's not used by across the company. Basically, during hackathon lots of people are working on machine learning projects that includes deep learning as well. So, there are lots of people who are using Keras for neural network implementation. And I have used this in my during my college and in company as well. We have used Keras to implement neural network for image recognition and in other things as well.,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.,As Keras works at a high level of abstraction, it limits the user to use it's own implemented algorithm. It doesn't give complete power to user to modify or implementing their own basic algorithm. Sometimes it is slow on GPU as compared to the pure tensorflow. Other than the above two cons, I don't think it has any negatives.,9,It made our development faster and easy as well. Sometime, when we need to change the basic algorithm, when we need to configure the neural network configuration then it doesn't allow us to modify that. As it comes with lot of inbuilt features of data processing, it is easy to process the data.,TensorFlow and Caffe Deep Learning Framework,TensorFlow, Theano, Caffe Deep Learning FrameworkBest wrapper library for TensorFlowKeras 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.,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.,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,9,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,Splunk Enterprise, New Relic Infrastructure, MATLAB
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Keras
8 Ratings
Score 9.0 out of 101
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Keras Reviews

Keras
8 Ratings
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Score 9.0 out of 101
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Gaurav Yadav profile photo
September 17, 2018

Keras Review: "Best wrapper library for TensorFlow and Theano."

Score 9 out of 10
Vetted Review
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Keras is being used during hackathon in my current company. And it's not used by across the company. Basically, during hackathon lots of people are working on machine learning projects that includes deep learning as well. So, there are lots of people who are using Keras for neural network implementation. And I have used this in my during my college and in company as well. We have used Keras to implement neural network for image recognition and in other things as well.
  • 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.
  • As Keras works at a high level of abstraction, it limits the user to use it's own implemented algorithm. It doesn't give complete power to user to modify or implementing their own basic algorithm.
  • Sometimes it is slow on GPU as compared to the pure TensorFlow.
  • Other than the above two cons, I don't think it has any negatives.
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.
Read Gaurav Yadav's full review
Raghuvar Prajapati profile photo
October 17, 2018

Keras Review: "Best wrapper library for TensorFlow"

Score 9 out of 10
Vetted Review
Verified User
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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.
  • 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.
  • 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
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.
Read Raghuvar Prajapati's full review

Keras Scorecard Summary

About Keras

Keras is a Python deep learning library
Categories:  Machine Learning

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