Overview

TensorFlow

Stacks3.8K

Followers3.5K

Votes106

GitHub Stars192.3K

Forks74.9K

H2O

Stacks121

Followers211

Votes8

GitHub Stars7.3K

Forks2.0K

Keras

Stacks1.1K

Followers1.1K

Votes22

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.

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Advice on TensorFlow, H2O, Keras

Xi

Developer at DCSIL

Oct 11, 2020

Decided

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.

99.3k views99.3k

Comments

Adithya

Student at PES UNIVERSITY

May 11, 2020

Needs advice

I have just started learning some basic machine learning concepts. So which of the following frameworks is better to use: Keras / TensorFlow/PyTorch. I have prior knowledge in python(and even pandas), java, js and C. It would be nice if something could point out the advantages of one over the other especially in terms of resources, documentation and flexibility. Also, could someone tell me where to find the right resources or tutorials for the above frameworks? Thanks in advance, hope you are doing well!!

107k views107k

Comments

philippe

Research & Technology & Innovation | Software & Data & Cloud | Professor in Computer Science

Sep 13, 2020

Review

Hello Amina, You need first to clearly identify the input data type (e.g. temporal data or not? seasonality or not?) and the analysis type (e.g., time series?, categories?, etc.). If you can answer these questions, that would be easier to help you identify the right tools (or Python libraries). If time series and Python, you have choice between Pendas/Statsmodels/Serima(x) (if seasonality) or deep learning techniques with Keras.

Good work, Philippe

4.63k views4.63k

Comments

Detailed Comparison

TensorFlow	H2O	Keras
TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.	H2O.ai is the maker behind H2O, the leading open source machine learning platform for smarter applications and data products. H2O operationalizes data science by developing and deploying algorithms and models for R, Python and the Sparkling Water API for Spark.	Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
-	-	neural networks API;Allows for easy and fast prototyping;Convolutional networks support;Recurent networks support;Runs on GPU
Statistics
GitHub Stars 192.3K	GitHub Stars 7.3K	GitHub Stars -
GitHub Forks 74.9K	GitHub Forks 2.0K	GitHub Forks -
Stacks 3.8K	Stacks 121	Stacks 1.1K
Followers 3.5K	Followers 211	Followers 1.1K
Votes 106	Votes 8	Votes 22
Pros & Cons
Pros 32 High Performance 19 Connect Research and Production 16 Deep Flexibility 12 Auto-Differentiation 11 True Portability Cons 9 Hard 6 Hard to debug 2 Documentation not very helpful	Pros 2 Very fast and powerful 2 Super easy to use 2 Highly customizable 2 Auto ML is amazing Cons 1 Not very popular	Pros 8 Quality Documentation 7 Supports Tensorflow and Theano backends 7 Easy and fast NN prototyping Cons 4 Hard to debug
Integrations
JavaScript	No integrations available	scikit-learn Python

What are some alternatives to TensorFlow, H2O, Keras?

scikit-learn

scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.

PyTorch

PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.

Kubeflow

The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions.

TensorFlow.js

Use flexible and intuitive APIs to build and train models from scratch using the low-level JavaScript linear algebra library or the high-level layers API

Polyaxon

An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications.

Streamlit

It is the app framework specifically for Machine Learning and Data Science teams. You can rapidly build the tools you need. Build apps in a dozen lines of Python with a simple API.

MLflow

MLflow is an open source platform for managing the end-to-end machine learning lifecycle.

PredictionIO

PredictionIO is an open source machine learning server for software developers to create predictive features, such as personalization, recommendation and content discovery.

Gluon

A new open source deep learning interface which allows developers to more easily and quickly build machine learning models, without compromising performance. Gluon provides a clear, concise API for defining machine learning models using a collection of pre-built, optimized neural network components.

Comet.ml

Comet.ml allows data science teams and individuals to automagically track their datasets, code changes, experimentation history and production models creating efficiency, transparency, and reproducibility.