Research any topic before you write.

Find related topics. | Discover entities. | See connections. | Build a topical map.

Apache MXNet

Apache MXNet is an open-source deep learning software framework that trains and deploys deep neural networks. It aims to be scalable, allows fast model training, and supports a flexible programming model and multiple programming languages (including C++, Python, Java, Julia, MATLAB, JavaScript, Go, R, Scala, Perl, and Wolfram Language). The MXNet library…

Products, Features & Overview

Use the mouse wheel or two fingers (on touchscreens) to zoom in and out of the map.

Research this topic

Explore the main themes, entities and connections around Apache MXNet. Start with the topic map, then use the sections below for research and deeper semantic analysis.

Explore this topic

Start with a few of the strongest sections from the source topic. These are research directions, not a list of keywords you must use.

Key facts & relationships

High-confidence facts extracted from structured source data. Use them as anchors for further research.

Developer
Apache Software Foundation
License
Apache License 2.0
Operating system
Windows, macOS, Linux
Repository
github.com/apache/incubator-mxnet
Stable release
1.9.1 / 10 May 2022; 4 years ago (10 May 2022)
Type
Library for machine learning and deep learning

Topics to explore

Browse the full topic structure. Each item opens a new analysis centered on that subject.

Overview

Features

Advanced semantic analysis

Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.

Map overview Semantic statistics

Apache MXNet

Nodes49
Edges48
Triples16
Avg. degree1.96
Density0.040816
Components1

How this topic connects Entity context

See the strongest relationship patterns around the current topic before diving into the raw triples.

Apache MXNet

Top relations

related to Features · 2
Apache MXNet → CNNs, LSTMs
Developer · 1
Apache MXNet → Apache Software Foundation
License · 1
Apache MXNet → Apache License 2.0
Operating system · 1
Apache MXNet → Windows, macOS, Linux
Repository · 1
Apache MXNet → github.com/apache/incubator-mxnet
Stable release · 1
Apache MXNet → 1.9.1 / 10 May 2022; 4 years ago (10 May 2022)
Type · 1
Apache MXNet → Library for machine learning and deep learning
Website · 1
Apache MXNet → mxnet.apache.org
Written in · 1
Apache MXNet → C++, Python, R, Java, Julia, JavaScript, Scala, Go, Perl
is a · 1
Apache MXNet → open-source deep learning software framework that trains and deploys deep neural networks

Important terminology Word statistics

Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.

Important terminology

mxnet apache multiple framework supports deep learning python julia javascript scala perl including software java go programming university 2023 frameworks

Entity relationships Subject–Predicate–Object triples

SubjectPredicateObjectConfidenceSrc
Apache MXNetDeveloperApache Software Foundation1.00infobox
Apache MXNetLicenseApache License 2.01.00infobox
Apache MXNetOperating systemWindows, macOS, Linux1.00infobox
Apache MXNetRepositorygithub.com/apache/incubator-mxnet1.00infobox
Apache MXNetStable release1.9.1 / 10 May 2022; 4 years ago (10 May 2022)1.00infobox
Apache MXNetTypeLibrary for machine learning and deep learning1.00infobox
Apache MXNetWebsitemxnet.apache.org1.00infobox
Apache MXNetWritten inC++, Python, R, Java, Julia, JavaScript, Scala, Go, Perl1.00infobox
Apache MXNetis aopen-source deep learning software framework that trains and deploys deep neural networks0.90text
Carnegie Melloninstance ofand research institutions0.80text
MITinstance ofand research institutions0.80text
the University of Washingtoninstance ofand research institutions0.80text

Related concept clusters Concept neighborhoods

These clusters group vocabulary that occurs around closely connected concepts in the source material.

    Connections between topic areas Semantic bridges

    Bridges can reveal useful research angles that are easy to miss in a flat list of related terms.

    Min side: 3
    For writers, content strategists, SEOs, marketers and creators — from quick topic research to advanced semantic analysis.