MXNet vs OpenNN

Overview

MXNet

Stacks49

Followers81

Votes2

OpenNN

Stacks5

Followers18

Votes0

MXNet vs OpenNN: What are the differences?

Developers describe MXNet as "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. On the other hand, OpenNN is detailed as "*A neural networks C++/Python library *". It is a free neural networks library for advanced analytics. It has solved many real-world applications in energy, marketing, health and more.

MXNet and OpenNN can be primarily classified as "Machine Learning" tools.

Some of the features offered by MXNet are:

Lightweight
Portable
Flexible distributed/Mobile deep learning

On the other hand, OpenNN provides the following key features:

Regression
Classification
Forecasting

MXNet is an open source tool with 17.9K GitHub stars and 6.38K GitHub forks. Here's a link to MXNet's open source repository on GitHub.

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Detailed Comparison

MXNet	OpenNN
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.	It is a free neural networks library for advanced analytics. It has solved many real-world applications in energy, marketing, health and more.
Lightweight;Portable;Flexible distributed/Mobile deep learning;	Regression; Classification; Forecasting
Statistics
Stacks 49	Stacks 5
Followers 81	Followers 18
Votes 2	Votes 0
Pros & Cons
Pros 2 User friendly	No community feedback yet
Integrations
Clojure Python Java JavaScript Scala Julia	Python C++

What are some alternatives to MXNet, 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.

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.

MXNet vs OpenNN