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H2O vs Keras vs TensorFlow: What are the differences?
Key Differences between H2O and Keras and TensorFlow
H2O is a scalable, in-memory data analysis and machine learning platform, while Keras and TensorFlow are popular deep learning frameworks. Although all three frameworks have their own strengths and are widely used in the machine learning community, there are some key differences between them.
Ease of Use: H2O provides an easy-to-use interface for data scientists, allowing them to build models with minimal coding. On the other hand, Keras and TensorFlow require more coding and are aimed at developers who want fine-grained control over their deep learning models.
Flexibility: Keras and TensorFlow offer more flexibility compared to H2O. They provide a wide range of deep learning architectures and allow customization at each layer of the neural network. H2O, while powerful, is more focused on ease of use and automating certain aspects of the modeling process.
Distributed Computing: H2O is designed for distributed computing and can handle large datasets that do not fit into memory. It can scale horizontally and run on clusters, making it suitable for big data applications. Keras and TensorFlow, on the other hand, rely on a single machine and are not specifically designed for distributed computing.
Ecosystem and Community Support: TensorFlow has a larger ecosystem and a stronger community support compared to H2O and Keras. It offers a variety of tools, libraries, and pre-trained models, making it easier to integrate with other frameworks and technologies. H2O has a growing community, but it is not as mature as the TensorFlow community.
Integration with Other Libraries: Keras can be used as a high-level API on top of TensorFlow, allowing users to leverage the power of both frameworks. H2O can also integrate with other machine learning and deep learning libraries, but it does not have the same level of integration as Keras and TensorFlow.
Compatibility and Portability: TensorFlow has official support for multiple programming languages, including Python, C++, Java, and more. This makes it easier to use TensorFlow in different environments and integrate it with existing systems. H2O primarily supports R and Python, while Keras supports Python.
In Summary, H2O is a user-friendly and scalable platform for data analysis and machine learning, while Keras and TensorFlow provide more flexibility and customization options for deep learning models. H2O is designed for distributed computing and can handle big data, while Keras and TensorFlow have larger ecosystems and stronger community support.
Pytorch is a famous tool in the realm of machine learning and it has already set up its own ecosystem. Tutorial documentation is really detailed on the official website. It can help us to create our deep learning model and allowed us to use GPU as the hardware support.
I have plenty of projects based on Pytorch and I am familiar with building deep learning models with this tool. I have used TensorFlow too but it is not dynamic. Tensorflow works on a static graph concept that means the user first has to define the computation graph of the model and then run the ML model, whereas PyTorch believes in a dynamic graph that allows defining/manipulating the graph on the go. PyTorch offers an advantage with its dynamic nature of creating graphs.
For my company, we may need to classify image data. Keras provides a high-level Machine Learning framework to achieve this. Specifically, CNN models can be compactly created with little code. Furthermore, already well-proven classifiers are available in Keras, which could be used as Transfer Learning for our use case.
We chose Keras over PyTorch, another Machine Learning framework, as our preliminary research showed that Keras is more compatible with .js. You can also convert a PyTorch model into TensorFlow.js, but it seems that Keras needs to be a middle step in between, which makes Keras a better choice.
For data analysis, we choose a Python-based framework because of Python's simplicity as well as its large community and available supporting tools. We choose PyTorch over TensorFlow for our machine learning library because it has a flatter learning curve and it is easy to debug, in addition to the fact that our team has some existing experience with PyTorch. Numpy is used for data processing because of its user-friendliness, efficiency, and integration with other tools we have chosen. Finally, we decide to include Anaconda in our dev process because of its simple setup process to provide sufficient data science environment for our purposes. The trained model then gets deployed to the back end as a pickle.
Pros of H2O
- Highly customizable2
- Very fast and powerful2
- Auto ML is amazing2
- Super easy to use2
Pros of Keras
- Quality Documentation8
- Supports Tensorflow and Theano backends7
- Easy and fast NN prototyping7
Pros of TensorFlow
- High Performance32
- Connect Research and Production19
- Deep Flexibility16
- Auto-Differentiation12
- True Portability11
- Easy to use6
- High level abstraction5
- Powerful5
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Cons of H2O
- Not very popular1
Cons of Keras
- Hard to debug4
Cons of TensorFlow
- Hard9
- Hard to debug6
- Documentation not very helpful2
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