TensorFlow.js vs ENorm | What are the differences? | StackShare

ENorm vs TensorFlow.js

Overview

TensorFlow.js

Stacks184

Followers378

Votes18

GitHub Stars19.0K

Forks2.0K

ENorm

Stacks1

Followers9

Votes0

GitHub Stars115

Forks13

ENorm vs TensorFlow.js: What are the differences?

ENorm: Equi-normalization of Neural Networks (by Facebook). A fast and iterative method for minimizing the L2 norm of the weights of a given neural network that provably converges to a unique solution; TensorFlow.js: Machine Learning in JavaScript. 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.

ENorm and TensorFlow.js belong to "Machine Learning Tools" category of the tech stack.

ENorm and TensorFlow.js are both open source tools. TensorFlow.js with 11.2K GitHub stars and 818 forks on GitHub appears to be more popular than ENorm with 104 GitHub stars and 8 GitHub forks.

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Detailed Comparison

TensorFlow.js	ENorm
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	A fast and iterative method for minimizing the L2 norm of the weights of a given neural network that provably converges to a unique solution.
-	Asymmetric scaling; Python 3.6 and latest support;
Statistics
GitHub Stars 19.0K	GitHub Stars 115
GitHub Forks 2.0K	GitHub Forks 13
Stacks 184	Stacks 1
Followers 378	Followers 9
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	No integrations available

What are some alternatives to TensorFlow.js, ENorm?

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.