TrustRadius Insights for Keras are summaries of user sentiment data from TrustRadius reviews and, when necessary, third party data sources.
Pros
Easy to use: Many users have found Keras to be easy to use, especially when implementing neural networks and deep learning models. They appreciate that with just one line of code, they can add a layer to the neural network with all its configurations.
Wide range of built-in features: Users appreciate that Keras provides a wide range of built-in features such as cov2d, conv2D, and maxPooling layers. This allows for fast development without the need to write everything from scratch.
Convenient mobile implementation: Several reviewers have mentioned that they find it convenient that Keras offers functionality to develop models on mobile devices. This is particularly helpful for users who require mobile implementation.
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Keras Reviews
3 Reviews
Engineering
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My general experience is positive. It may give some new software engineers a marginally misshaped Idea of how things work - since it is genuinely simple to building an amazing neural network with it, yet it could likewise urge them to burrow further. Building even a basic NN with C without any preparation would disappoint most fledglings, so this is a decent spot for understudies to begin - accepting that they're likewise examining hypothesis.
Pros
Until we have IDEs that can make an interpretation of our idea into code, I don't think making Deep Learning models could be made a lot simpler.
It's makes the process easy for building the Neural Network.
Doesn't require to have strong background in Deep Learning.
Cons
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.
Likelihood to Recommend
On the off chance that you are new to Deep learning and need to figure out how to code, Keras is a decent beginning, since it is easy to use and very handy to learn API.
Keras isn't utilized over the entire association, however it is being utilized by a portion of our specialties. In those offices, the vast majority of them are utilizing it to do some sort of AI task, which fundamentally incorporates planning and actualizing the neural organization. I have utilized this for loads of reasons. Every one of them was in AI fields, similar to picture preparing, essential grouping, and considerably more.
Pros
Easy to use. We can implement neural networks easily.
There is a lot of built-in utility that makes the task easier.
It also supports TensorFlow.
Cons
We can't modify everything that we want to.
Some built-in model can be included as a part of this library.
Resource requirement is quite high for using this library.
Likelihood to Recommend
I would suggest using it when anybody needs to rapidly build up a neural network for the organization. Or if a client is tackling any AI issue that incorporates machine learning--image classification, face recognition, or doing some content examination which incorporates LSTM or some other calculation. It is not recommended if you want to change the algorithm internally, as it won't allow you to do so.
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.
Pros
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.
Cons
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.
Likelihood to Recommend
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.
