Likelihood to Recommend Most suited if in little time you wanted to build and train a model. Then, H2O makes life very simple. It has support with R, Python and Java, so no programming dependency is required to use it. It's very simple to use. If you want to modify or tweak your ML algorithm then H2O is not suitable. You can't develop a model from scratch.
Read full review 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 Read full review Pros Excellent analytical and prediction tool In the beginning, usage of H20 Flow in Web UI enables quick development and sharing of the analytical model Readily available algorithms, easy to use in your analytical projects Faster than Python scikit learn (in machine learning supervised learning area) It can be accessed (run) from Python, not only JAVA etc. Well documented and suitable for fast training or self studying In the beginning, one can use the clickable Flow interface (WEB UI) and later move to a Python console. There is then no need to click in H20 Flow It can be used as open source Read full review 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. Read full review Cons Better documentation Improve the Visual presentations including charting etc Read full review 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 Read full review Usability I am giving this rating depending on my experience so far with Keras, I didn't face any issue far. I would like to recommend it to the new developers.
Read full review Support Rating The overall experience I have with H2O is really awesome, even with its cost effectiveness.
Read full review 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.
Read full review Alternatives Considered Both are open source (though H2O only up to some level). Both comprise of deep learning, but H2O is not focused directly on deep learning, while Tensor Flow has a "laser" focus on deep learning. H2O is also more focused on scalability. H2O should be looked at not as a competitor but rather a complementary tool. The use case is usually not only about the algorithms, but also about the data model and data logistics and accessibility. H2O is more accessible due to its UI. Also, both can be accessed from Python. The community around
TensorFlow seems larger than that of H2O.
Read full review 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.
Read full review Return on Investment Positive impact: saving in infrastructure expenses - compared to other bulky tools this costs a fraction Positive impact: ability to get quick fixes from H2O when problems arise - compared to waiting for several months/years for new releases from other vendors Positive impact: Access to H2O core team and able to get features that are needed for our business quickly added to the core H2O product Read full review Easy and faster way to develop neural network. It would be much better if it is available in Java. It doesn't allow you to modify the internal things. Read full review ScreenShots