Keras Professional, Scientific, and Technical Services Reviews & Insights

Score7 out of 10

19 Reviews and Ratings

Community insights

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.

Keras Reviews

4 Reviews

Professional, Scientific, and Technical ServicesInformation Technology & Services4

Keras Review

Rating: 9 out of 10

Incentivized

November 10, 2020

Use Cases and Deployment Scope

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.

Saurabh Kumar

Software Engineer in Engineering at Dunzo (Information Technology & Services, 1001-5000 employees)

Vetted Review

1 year of experience

Rapidly build neural network

Rating: 8 out of 10

Incentivized

November 9, 2020

Use Cases and Deployment Scope

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.

Rakesh Kumar

Software Engineer in Engineering at Vahak (Information Technology & Services, 201-500 employees)

Vetted Review

1 year of experience

Best wrapper library for TensorFlow and Theano

Rating: 8 out of 10

Incentivized

January 18, 2019

Use Cases and Deployment Scope

Keras is not used across the whole organization, but it is being used by some of our departments only. And in those departments, most of them are using it to do some kind of machine learning task, which basically includes designing and implementing the neural network. I have used this for lots of reasons. All of them were of machine learning fields, like image processing, basic classification, and much more.

Pros

Performs well when you are doing some implementation which requires neural network implementation and some other deep learning models
It has lots of inbuilt tools which you can have clean your data before processing
It supports Tensorflow as its backend, so it can easily use GPU

Cons

As it is a kind of wrapper library it won't allow you to modify everything of its backend
Unlike other deep learning libraries, it lacks a pre-defined trained model to use
Errors thrown are not always very useful for debugging. Sometimes it is difficult to know the root cause just with the logs

Likelihood to Recommend

There are a lot of other libraries in competition with Keras, like TensorFlow, tfLearn, theano and lot more. If you are new to the deep learning field and want to learn things quickly with implementation then I think you should start with Keras. Once you have good enough knowledge about deep learning concepts then you can shift to some other library, most probably to TensorFlow which gives you the power to write and customize whole neural network

Rounak Jangir

Ab Initio Developer in Research & Development at Tata Consultancy Services (Information Technology and Services, 10,001+ employees)

Vetted Review

1 year of experience

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Best wrapper library for TensorFlow and Theano.

Rating: 9 out of 10

Incentivized

September 17, 2018

Use Cases and Deployment Scope

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.

Gaurav Yadav

Software Developer in Engineering at COVIAM (Information Technology and Services, 201-500 employees)

Vetted Review

2 years of experience

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Best wrapper library for TensorFlow and Theano.

Rating: 9 out of 10

Incentivized

September 17, 2018

Use Cases and Deployment Scope

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

Gaurav Yadav

Software Developer in Engineering at COVIAM (Information Technology and Services, 201-500 employees)

Vetted Review

2 years of experience

View profile