Keras is being used together with TensorFlow, in our new Data & AIML department. Many business use cases are implemented on top of it. We …
My general experience is positive. It may give some new software engineers a marginally misshaped Idea of how things work - since it is …
Keras isn't utilized over the entire association, however it is being utilized by a portion of our specialties. In those offices, the vast …
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 …
Keras is being used to develop data science models for predictions that include implementing neural networks and others as well. It is not …
Keras is being used during hackathon in my current company. And it's not used by across the company. Basically, during hackathon lots of …
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Keras is a Python deep learning library
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Keras is being used together with TensorFlow, in our new Data & AIML department. Many business use cases are implemented on top of it. We use Keras as high level API, responsible for model designing, model training, model evaluation and model inference/prediction. It is part of our final Model As Service product, deployed in our production environment, allowing various business application [to] consume its prediction output and take the important decision as early as possible.
- As the high level API, clean and neat, allowing the data scientists to develop the standard deep learning model very quickly (using the mature and existed algorithm module blocks)
- Seamlessly integrate with [a] couple of Deep Learning framework, although we only use TensorFlow, and since TF2.0 Keras has become the 1st class native API
- Keras model itself is not thread safe, and the documentation regarding how the multi thread works during the prediction time is not quite clear and enough, we still need [to] tweak the TensorFlow old (session + graph) way to support our Model As Service, in the high concurrent scenario, but with limited memory constraint.
- Some API and default implementation still has a lot [of] improvement room, for example, the checkpoint callback, only saves the "best" model, natively can not save all other metadata of the "best" model, we currently have to extend that by ourselves to meet our project requirement.
- Some advanced topic, like distributed training, documentation is not so clear and very hard to find the real code example.
Keras is quite perfect, if the aim is to build the standard Deep Learning model, and materialize it to serve the real business use case, while it is not suitable if the purpose is for research and a lot of non-standard try out and customization are required, in that case either directly goes to low level TensorFlow API or Pytorch
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.
- 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.
- 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.
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 have really good support along with the strong community over the internet. So in case you stuck, It won't so hard to get out from it.
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.
- 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.
- 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.
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.
It has good customer support. Once I was stuck on issue. It was resolved within a week.
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.
- 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
- 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
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
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.
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.