ENorm vs MXNet

Overview

MXNet

Stacks49

Followers81

Votes2

ENorm

Stacks1

Followers9

Votes0

GitHub Stars115

Forks13

ENorm vs MXNet: What are the differences?

What is 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.

What is MXNet? A flexible and efficient library for deep learning. A deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, it contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly.

ENorm and MXNet can be categorized as "Machine Learning" tools.

ENorm and MXNet are both open source tools. MXNet with 17.5K GitHub stars and 6.21K forks on GitHub appears to be more popular than ENorm with 107 GitHub stars and 9 GitHub forks.

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CLI (Node.js)

Manual

Detailed Comparison

MXNet	ENorm
A deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, it contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly.	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.
Lightweight;Portable;Flexible distributed/Mobile deep learning;	Asymmetric scaling; Python 3.6 and latest support;
Statistics
GitHub Stars -	GitHub Stars 115
GitHub Forks -	GitHub Forks 13
Stacks 49	Stacks 1
Followers 81	Followers 9
Votes 2	Votes 0
Pros & Cons
Pros 2 User friendly	No community feedback yet
Integrations
Clojure Python Java JavaScript Scala Julia	No integrations available

What are some alternatives to MXNet, 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.

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.

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.

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