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Chainer

Chainer is an open source deep learning framework written purely in Python on top of NumPy and CuPy Python libraries. The development is led by Japanese venture company Preferred Networks in partnership with IBM, Intel, Microsoft, and Nvidia.

Applications, Art & Companies

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Research this topic

Explore the main themes, entities and connections around Chainer. 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.

Define-by-run

7 related topics

Applications

2 related topics

Overview

12 related topics

Extension libraries

2 related topics

Key facts & relationships

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

Available in
Python
Developers
Community, Preferred Networks, Inc.
License
MIT
Original author
Seiya Tokui
Platform
cross-platform
Release
June 9, 2015; 11 years ago (2015-06-09).

Topics to explore

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

Overview

Define-by-run

Extension libraries

Applications

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

Chainer

Nodes28
Edges27
Triples35
Avg. degree1.93
Density0.071429
Components1

How this topic connects Entity context

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

Chainer

Top relations

related to Define-by-run · 8
Chainer → If, In, On, One, TensorFlow, The, Theano, This
related to Extension libraries · 8
Chainer → ChainerCV, ChainerMN, ChainerRL, ChainerUI, Facebook, GPUs, ImageNet, ResNet-50
Available in · 1
Chainer → Python
Developers · 1
Chainer → Community, Preferred Networks, Inc.
License · 1
Chainer → MIT
Original author · 1
Chainer → Seiya Tokui
Platform · 1
Chainer → cross-platform
Release · 1
Chainer → June 9, 2015; 11 years ago (2015-06-09).
Repository · 1
Chainer → github.com/chainer/chainer
Stable release · 1
Chainer → 7.8.1 / 5 January 2022; 4 years ago (5 January 2022)

Important terminology Word statistics

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

Important terminology

learning approach define-by-run network deep networks python preferred libraries written training calculation framework open source top development large pytorch actual

Entity relationships Subject–Predicate–Object triples

SubjectPredicateObjectConfidenceSrc
ChainerAvailable inPython1.00infobox
ChainerDevelopersCommunity, Preferred Networks, Inc.1.00infobox
ChainerLicenseMIT1.00infobox
ChainerOriginal authorSeiya Tokui1.00infobox
ChainerPlatformcross-platform1.00infobox
ChainerReleaseJune 9, 2015; 11 years ago (2015-06-09).1.00infobox
ChainerRepositorygithub.com/chainer/chainer1.00infobox
ChainerStable release7.8.1 / 5 January 2022; 4 years ago (5 January 2022)1.00infobox
ChainerTypeDeep learning library1.00infobox
ChainerWebsitechainer.org1.00infobox
ChainerWritten inPython1.00infobox
Chaineris aopen source deep learning framework written purely in Python on top of NumPy and CuPy Python libraries0.90text

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.