OpenNN vs TensorFlow.js

Overview

TensorFlow.js

Stacks184

Followers378

Votes18

GitHub Stars19.0K

Forks2.0K

OpenNN

Stacks5

Followers18

Votes0

OpenNN vs TensorFlow.js: What are the differences?

Introduction: OpenNN and TensorFlow.js are two popular frameworks used in the field of machine learning. Both tools offer functionalities for developing and deploying machine learning models, however, they have distinct differences that set them apart.

Programming Language: OpenNN is a C++ library while TensorFlow.js is a JavaScript library. This difference in programming language can affect the ease of integration with existing systems and the overall development process based on the language preferences and expertise of the users.
Deployment Environment: OpenNN is primarily designed for deployment on desktop systems while TensorFlow.js is optimized for web-based applications. This difference makes TensorFlow.js more suitable for applications requiring online deployment and interaction through web browsers.
Complexity and Learning Curve: TensorFlow.js offers a higher level of abstraction and more pre-built functionalities compared to OpenNN, which can result in a steeper learning curve for beginners. OpenNN, on the other hand, provides a lower-level interface that allows for more fine-grained control over the machine learning models.
Community Support and Ecosystem: TensorFlow.js benefits from the extensive community support and a well-established ecosystem of tools, resources, and pre-trained models. OpenNN, being a smaller library, may have a more limited community and fewer resources available for users.
Performance and Scalability: TensorFlow.js is known for its scalability and performance optimizations, especially when working with large datasets or complex neural network architectures. OpenNN, while efficient, may not offer the same level of scalability and performance optimizations as TensorFlow.js.
Hardware Acceleration: TensorFlow.js supports hardware acceleration using WebGL, which can significantly enhance the execution speed of machine learning models in the browser. OpenNN, being a C++ library, may not have built-in support for hardware acceleration through web technologies.

In Summary, OpenNN and TensorFlow.js differ in programming language, deployment environment, complexity, community support, performance, and hardware acceleration capabilities.

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CLI (Node.js)

Manual

Detailed Comparison

TensorFlow.js	OpenNN
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	It is a free neural networks library for advanced analytics. It has solved many real-world applications in energy, marketing, health and more.
-	Regression; Classification; Forecasting
Statistics
GitHub Stars 19.0K	GitHub Stars -
GitHub Forks 2.0K	GitHub Forks -
Stacks 184	Stacks 5
Followers 378	Followers 18
Votes 18	Votes 0
Pros & Cons
Pros 6 Open Source 5 NodeJS Powered 2 Deploy python ML model directly into javascript 1 Cost - no server needed for inference 1 Easy to share and use - get more eyes on your research	No community feedback yet
Integrations
JavaScript TensorFlow	Python C++

What are some alternatives to TensorFlow.js, OpenNN?

TensorFlow

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.

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.

Keras

Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/

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.

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.

H2O

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.

PredictionIO

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

Related Comparisons

OpenNN vs TensorFlow.js: What are the differences?

Programming Language: OpenNN is a C++ library while TensorFlow.js is a JavaScript library. This difference in programming language can affect the ease of integration with existing systems and the overall development process based on the language preferences and expertise of the users.
Deployment Environment: OpenNN is primarily designed for deployment on desktop systems while TensorFlow.js is optimized for web-based applications. This difference makes TensorFlow.js more suitable for applications requiring online deployment and interaction through web browsers.
Complexity and Learning Curve: TensorFlow.js offers a higher level of abstraction and more pre-built functionalities compared to OpenNN, which can result in a steeper learning curve for beginners. OpenNN, on the other hand, provides a lower-level interface that allows for more fine-grained control over the machine learning models.
Community Support and Ecosystem: TensorFlow.js benefits from the extensive community support and a well-established ecosystem of tools, resources, and pre-trained models. OpenNN, being a smaller library, may have a more limited community and fewer resources available for users.
Performance and Scalability: TensorFlow.js is known for its scalability and performance optimizations, especially when working with large datasets or complex neural network architectures. OpenNN, while efficient, may not offer the same level of scalability and performance optimizations as TensorFlow.js.
Hardware Acceleration: TensorFlow.js supports hardware acceleration using WebGL, which can significantly enhance the execution speed of machine learning models in the browser. OpenNN, being a C++ library, may not have built-in support for hardware acceleration through web technologies.

In Summary, OpenNN and TensorFlow.js differ in programming language, deployment environment, complexity, community support, performance, and hardware acceleration capabilities.

OpenNN vs TensorFlow.js

Overview