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Torch (machine learning): Applications & Science

Torch is an open-source machine learning library, a scientific computing framework, and a scripting language based on Lua. It provides LuaJIT interfaces to deep learning algorithms implemented in C. It was created by the Idiap Research Institute. Torch development moved in 2017 to PyTorch, a port of the library to Python.

Language: English [EN]
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Torch (machine learning) topic overview

The analysis highlights Applications and Science as prominent areas in the source structure around Torch (machine learning).

Related topics
45
Source areas
5
Connected nodes
50
Extracted relationships
13
Concept neighborhoods
21
Bridge connections
50

What this topic covers Research coverage

Source areas are shown by the number of related topics found in each part of the analysis. Use smaller areas too: they can reveal specialized angles and content gaps.

Torch · 17 topics
Nn · 11 topics
Overview · 10 topics
Applications · 6 topics
Other packages · 1 topics

Smaller areas are not necessarily less important. They contain fewer connections in this analysis and can be useful for finding specialized angles or coverage gaps.

Key facts & relationships

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

Final release
7.0 / February 27, 2017; 9 years ago (2017-02-27)
License
BSD License
Operating system
Linux, Android, Mac OS X, iOS
Original authors
Ronan Collobert, Samy Bengio, Johnny Mariéthoz
Release
October 2002; 23 years ago (2002-10)
Repository
github.com/torch/torch7

Explore all related topics Closing gaps

Browse the complete topic structure, not only the most central items. Less prominent entities and concepts can reveal missing angles, specialized context and useful research gaps. Each item opens a new analysis centered on that subject.

Overview

Torch

Nn

Other packages

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.

How Torch (machine learning) connects Entity context

The extracted context around Torch (machine learning) shows recurring relationship patterns in the source. For example, Torch (machine learning) → 7.0 / February 27, 2017; 9 years ago (2017-02-27) Another extracted example is Torch (machine learning) → BSD License. Use these groups to spot repeated connection types before inspecting the individual relationships.

Torch (machine learning)

Top relations

Final release · 1
Torch (machine learning) → 7.0 / February 27, 2017; 9 years ago (2017-02-27)
License · 1
Torch (machine learning) → BSD License
Operating system · 1
Torch (machine learning) → Linux, Android, Mac OS X, iOS
Original authors · 1
Torch (machine learning) → Ronan Collobert, Samy Bengio, Johnny Mariéthoz
Release · 1
Torch (machine learning) → October 2002; 23 years ago (2002-10)
Repository · 1
Torch (machine learning) → github.com/torch/torch7
Type · 1
Torch (machine learning) → Library for machine learning and deep learning
Website · 1
Torch (machine learning) → torch.ch
Written in · 1
Torch (machine learning) → Lua, C, C++

Important terminology

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

Important terminology

torch lua also used learning library packages modules provides deep using neural implemented object pytorch machine open-source android ios basic

Torch (machine learning) relationships Subject–Predicate–Object triples

TTTA extracted 13 structured relationships around Torch (machine learning). Examples in this analysis include Torch (machine learning) → Final release → 7.0 / February 27, 2017; 9 years ago (2017-02-27) and Torch (machine learning) → License → BSD License. The table shows each extracted connection, where it came from and its confidence.

SubjectPredicateObjectConfidenceSrc
Torch (machine learning)Final release7.0 / February 27, 2017; 9 years ago (2017-02-27)1.00infobox
Torch (machine learning)LicenseBSD License1.00infobox
Torch (machine learning)Operating systemLinux, Android, Mac OS X, iOS1.00infobox
Torch (machine learning)Original authorsRonan Collobert, Samy Bengio, Johnny Mariéthoz1.00infobox
Torch (machine learning)ReleaseOctober 2002; 23 years ago (2002-10)1.00infobox
Torch (machine learning)Repositorygithub.com/torch/torch71.00infobox
Torch (machine learning)TypeLibrary for machine learning and deep learning1.00infobox
Torch (machine learning)Websitetorch.ch1.00infobox
Torch (machine learning)Written inLua, C, C++1.00infobox
parallelisminstance ofThese extra packages provide a wide range of utilities0.80text
asynchronous input/outputinstance ofThese extra packages provide a wide range of utilities0.80text
image processinginstance ofThese extra packages provide a wide range of utilities0.80text

Related concept clusters Concept neighborhoods

The concept neighborhoods around Torch (machine learning) bring nearby vocabulary together. In this analysis, examples include Library, Learning and Machine. Use the clusters to find adjacent concepts and terminology that may deserve separate research.

  • Torch (machine learning)
    • Library
    • Learning
    • Machine
    • Python
    • Android
    • Computing
    • Ios
    • Lua
    • Open-source
    • Pytorch
    • References
    • Used
  • torch (machine learning)
    • Deep
    • Library
    • Learning
    • Machine
    • Open-source
    • Python
    • Pytorch
    • Website
    • Android
    • Computing
    • Core
    • Ios
  • lua
    • Torch
    • Also
    • Called
    • Machine
    • Package
    • References
    • Python
    • Android
    • Core
    • Created
    • Function
    • Ios
  • machine learning
    • Deep
    • Library
    • Learning
    • Machine
    • Open-source
    • Python
    • Pytorch
    • Website
    • Android
    • Computing
    • Core
    • Ios
  • deep learning
    • Deep
    • Learning
    • Pytorch
    • Website
    • Machine
    • Open-source
    • Library
    • Luajit
    • Python
    • Android
    • Core
    • Ios
  • android
    • Ios
    • Python
    • Core
    • Machine
    • Package
    • Pytorch
    • References
    • Website
    • Deep
    • Library
    • Like
    • Learning
  • ios
    • Python
    • Core
    • Machine
    • Package
    • Pytorch
    • References
    • Website
    • Library
    • Like
    • Learning
    • Neural
    • Torch
  • other packages
    • Core
    • Used
    • Torch
    • Also
    • Python
    • Android
    • Functions
    • Ios
    • Package
    • Pytorch
    • References
    • Website

Connections between topic areas Semantic bridges

For Torch (machine learning), one of the stronger structural bridges in this analysis connects Torch (machine learning) with Torch. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.

Min side: 3
Torch (machine learning) — Torch · splits 33 ⟂ 18
Torch (machine learning) — Nn · splits 39 ⟂ 12
Torch (machine learning) — Overview · splits 40 ⟂ 11
Torch (machine learning) — Applications · splits 44 ⟂ 7

Map overview Semantic statistics

Torch (machine learning)

Nodes51
Edges50
Triples13
Avg. degree1.96
Density0.039216
Components1

Source & methodology

TTTA analyzes the structure around Torch (machine learning) to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as Applications & Science, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.

Source: Wikipedia — Torch (machine learning) · EN edition · Analysis: TopicsToTalkAbout

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