fastai
E701495
fastai is a high-level deep learning library built on top of PyTorch that simplifies training and deploying state-of-the-art machine learning models.
All labels observed (1)
| Label | Occurrences |
|---|---|
| fastai canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T7874735 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: fastai Context triple: [Adam optimizer, implementedIn, fastai]
-
A.
PyTorch
PyTorch is an open-source deep learning framework widely used for building and training neural networks, known for its dynamic computation graph and strong support for research and production in Python.
-
B.
torchvision (ecosystem)
torchvision is a PyTorch-based computer vision library providing datasets, model architectures, and image transformations commonly used for training and evaluating deep learning models.
-
C.
Hugging Face Accelerate
Hugging Face Accelerate is a lightweight library that simplifies running and scaling PyTorch and Transformers models across CPUs, GPUs, and distributed hardware with minimal code changes.
-
D.
Hugging Face Transformers
Hugging Face Transformers is a widely used open-source library that provides state-of-the-art transformer-based models and tools for natural language processing and related machine learning tasks.
-
E.
FasterRCNN
FasterRCNN is a popular two-stage object detection architecture that first proposes candidate regions and then classifies and refines bounding boxes, widely used in computer vision tasks.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: fastai Target entity description: fastai is a high-level deep learning library built on top of PyTorch that simplifies training and deploying state-of-the-art machine learning models.
-
A.
PyTorch
PyTorch is an open-source deep learning framework widely used for building and training neural networks, known for its dynamic computation graph and strong support for research and production in Python.
-
B.
torchvision (ecosystem)
torchvision is a PyTorch-based computer vision library providing datasets, model architectures, and image transformations commonly used for training and evaluating deep learning models.
-
C.
Hugging Face Accelerate
Hugging Face Accelerate is a lightweight library that simplifies running and scaling PyTorch and Transformers models across CPUs, GPUs, and distributed hardware with minimal code changes.
-
D.
Hugging Face Transformers
Hugging Face Transformers is a widely used open-source library that provides state-of-the-art transformer-based models and tools for natural language processing and related machine learning tasks.
-
E.
FasterRCNN
FasterRCNN is a popular two-stage object detection architecture that first proposes candidate regions and then classifies and refines bounding boxes, widely used in computer vision tasks.
- F. None of above. chosen
Statements (47)
| Predicate | Object |
|---|---|
| instanceOf |
Python library
ⓘ
deep learning library ⓘ open-source software ⓘ |
| basedOn | PyTorch NERFINISHED ⓘ |
| developer |
Jeremy Howard
NERFINISHED
ⓘ
Sylvain Gugger NERFINISHED ⓘ fast.ai NERFINISHED ⓘ |
| documentation | https://docs.fast.ai ⓘ |
| feature |
GPU acceleration via PyTorch
ⓘ
built-in data augmentation ⓘ callbacks system ⓘ data block API ⓘ high-level API over PyTorch ⓘ learning rate finder ⓘ metrics and logging utilities ⓘ mixed-precision training ⓘ one-cycle training policy ⓘ transfer learning utilities ⓘ |
| hasSubmodule |
fastai.collab
ⓘ
fastai.tabular NERFINISHED ⓘ fastai.text NERFINISHED ⓘ fastai.vision NERFINISHED ⓘ |
| integratesWith |
Jupyter Notebook
NERFINISHED
ⓘ
PyTorch ecosystem NERFINISHED ⓘ |
| license | Apache License 2.0 ⓘ |
| maintainer | fast.ai organization NERFINISHED ⓘ |
| programmingLanguage | Python ⓘ |
| purpose |
simplify deployment of deep learning models
ⓘ
simplify training of deep learning models ⓘ |
| relatedTo |
Practical Deep Learning for Coders
NERFINISHED
ⓘ
fastai course ⓘ |
| repository | https://github.com/fastai/fastai ⓘ |
| supportsModel |
convolutional neural networks
ⓘ
matrix factorization models ⓘ recurrent neural networks ⓘ tabular neural networks ⓘ transformer models ⓘ |
| supportsPlatform |
Linux
ⓘ
Windows ⓘ macOS ⓘ |
| supportsTask |
collaborative filtering
ⓘ
computer vision ⓘ natural language processing ⓘ tabular data modeling ⓘ time series modeling ⓘ |
| usedFor |
educational purposes in deep learning courses
ⓘ
rapid prototyping of deep learning models ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: fastai Description of subject: fastai is a high-level deep learning library built on top of PyTorch that simplifies training and deploying state-of-the-art machine learning models.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.