Research this topic
Explore the main themes, entities and connections around Torch (machine learning). 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.
Torch
Nn
Applications
Other packages
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
Topics to explore
A structured outline of related entities, concepts and subtopics. Open any item to build a new map centered on it.Browse the full topic structure. Each item opens a new analysis centered on that subject.
Overview
- Open-source Open-source software
- Machine learning
- Scientific computing
- Scripting language
- Lua Lua (programming language)
- LuaJIT
- Deep learning
- Idiap Research Institute IDIAP
- PyTorch
- Python Python (programming language)
Torch
- Tensor Tensor (machine learning)
- Uniform Uniform distribution (continuous)
- Normal Normal distribution
- Multinomial Multinomial distribution
- Basic linear algebra subprogram Basic Linear Algebra Subprograms
- Dot product
- Matrix–vector multiplication
- Matrix–matrix multiplication Matrix multiplication
- Matrix product
- REPL
- Object-oriented programming
- Serialization
- Object factories Factory method pattern
- Classes Class (programming)
- Constructor Constructor (object-oriented programming)
- Object Object (computer science)
- Coroutines Coroutine
Nn
- Neural networks Neural network
- Feedforward Feedforward neural network
- Backpropagate Backpropagation
- Composites Composite pattern
- Automatic gradient differentiation Automatic differentiation
- Multilayer perceptron
- Loss functions Loss function
- Mean squared error
- Cross-entropy
- Stochastic gradient descent
- Regularization Regularization (mathematics)
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.
Map overview Semantic statistics
Number of nodes, edges, triples, density and central hubs. Use it to gauge the size and connectivity of the map.Torch (machine learning)
How this topic connects Entity context
Quick relationship hints grouped by predicate. Useful for spotting recurring semantic connections around the current entity.See the strongest relationship patterns around the current topic before diving into the raw triples.
Torch (machine learning)
Top relations
Important terminology Word statistics
Frequent words and multi-word phrases across the lead, headings, infobox and body. Useful for terminology coverage.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
Entity relationships Subject–Predicate–Object triples
Extracted RDF-like relationships with confidence and source. The table includes structured facts and lower-confidence contextual relations.| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| Torch (machine learning) | Final release | 7.0 / February 27, 2017; 9 years ago (2017-02-27) | 1.00 | infobox |
| Torch (machine learning) | License | BSD License | 1.00 | infobox |
| Torch (machine learning) | Operating system | Linux, Android, Mac OS X, iOS | 1.00 | infobox |
| Torch (machine learning) | Original authors | Ronan Collobert, Samy Bengio, Johnny Mariéthoz | 1.00 | infobox |
| Torch (machine learning) | Release | October 2002; 23 years ago (2002-10) | 1.00 | infobox |
| Torch (machine learning) | Repository | github.com/torch/torch7 | 1.00 | infobox |
| Torch (machine learning) | Type | Library for machine learning and deep learning | 1.00 | infobox |
| Torch (machine learning) | Website | torch.ch | 1.00 | infobox |
| Torch (machine learning) | Written in | Lua, C, C++ | 1.00 | infobox |
| parallelism | instance of | These extra packages provide a wide range of utilities | 0.80 | text |
| asynchronous input/output | instance of | These extra packages provide a wide range of utilities | 0.80 | text |
| image processing | instance of | These extra packages provide a wide range of utilities | 0.80 | text |
Related concept clusters Concept neighborhoods
Clusters of nearby vocabulary surrounding the topic. Scan them for adjacent concepts and language you may have missed.These clusters group vocabulary that occurs around closely connected concepts in the source material.
Connections between topic areas Semantic bridges
Bridge nodes connect otherwise separate parts of the map. Expand a row to inspect the topic groups on each side.Bridges can reveal useful research angles that are easy to miss in a flat list of related terms.