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.zenodo.json
{ "description": "TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML-powered applications.", "license": "Apache-2.0", "title": "TensorFlow", "upload_type": "software", "creators": [ { "name": "TensorFlow Developers" }Created: Tue Dec 30 12:39:10 GMT 2025 - Last Modified: Tue May 18 19:19:25 GMT 2021 - 741 bytes - Click Count (0) -
cmd/erasure-metadata-utils.go
func readAllFileInfo(ctx context.Context, disks []StorageAPI, origbucket string, bucket, object, versionID string, readData, healing bool) ([]FileInfo, []error) { metadataArray := make([]FileInfo, len(disks)) opts := ReadOptions{ ReadData: readData, Healing: healing, } g := errgroup.WithNErrs(len(disks)) // Read `xl.meta` in parallel across disks. for index := range disks {
Created: Sun Dec 28 19:28:13 GMT 2025 - Last Modified: Fri Aug 29 02:39:48 GMT 2025 - 11.7K bytes - Click Count (0) -
docs/es/docs/_llm-test.md
* <abbr title="Un método de machine learning que usa redes neuronales artificiales con numerosas capas ocultas entre las capas de entrada y salida, desarrollando así una estructura interna completa">Deep Learning</abbr> ### El abbr da una frase completa y una explicación { #the-abbr-gives-a-full-phrase-and-an-explanation }
Created: Sun Dec 28 07:19:09 GMT 2025 - Last Modified: Tue Dec 16 16:16:35 GMT 2025 - 12.6K bytes - Click Count (0) -
compat/maven-compat/src/main/java/org/apache/maven/project/path/DefaultPathTranslator.java
// Take out basedir expression and the leading slash s = chopLeadingFileSeparator(s.substring(basedirExpr.length())); } else { s = "."; } } } return s; } /** * Removes the leading directory separator from the specified filesystem path (if any). For platform-independentCreated: Sun Dec 28 03:35:09 GMT 2025 - Last Modified: Fri Jun 06 14:28:57 GMT 2025 - 7.2K bytes - Click Count (0) -
docs/de/docs/_llm-test.md
* <abbr title="Eine Methode des Machine Learning, die künstliche neuronale Netze mit zahlreichen versteckten Schichten zwischen Eingabe- und Ausgabeschicht verwendet und so eine umfassende interne Struktur entwickelt">Deep Learning</abbr> ### Das abbr gibt eine vollständige Phrase und eine Erklärung { #the-abbr-gives-a-full-phrase-and-an-explanation }
Created: Sun Dec 28 07:19:09 GMT 2025 - Last Modified: Wed Dec 17 07:17:04 GMT 2025 - 12.6K bytes - Click Count (0) -
internal/s3select/sql/parser.go
} // UnaryTerm represents a single negated term or a primary term type UnaryTerm struct { Negated *NegatedTerm `parser:" @@"` Primary *PrimaryTerm `parser:"| @@"` } // NegatedTerm has a leading minus sign. type NegatedTerm struct { Term *PrimaryTerm `parser:"\"-\" @@"` } // PrimaryTerm represents a Value, Path expression, a Sub-expression // or a function call. type PrimaryTerm struct {Created: Sun Dec 28 19:28:13 GMT 2025 - Last Modified: Thu Jan 18 07:03:17 GMT 2024 - 12.9K bytes - Click Count (0) -
docs/tr/docs/project-generation.md
... müsaitliğime ve diğer faktörlere bağlı olarak daha sonra gelebilir. 😅 🎉 ## Machine Learning modelleri, spaCy ve FastAPI GitHub: <a href="https://github.com/microsoft/cookiecutter-spacy-fastapi" class="external-link" target="_blank">https://github.com/microsoft/cookiecutter-spacy-fastapi</a> ### Machine Learning modelleri, spaCy ve FastAPI - Features * **spaCy** NER model entegrasyonu.
Created: Sun Dec 28 07:19:09 GMT 2025 - Last Modified: Mon Jul 29 23:35:07 GMT 2024 - 6K bytes - Click Count (0) -
docs/fr/docs/async.md
* L'apprentissage automatique (ou **Machine Learning**) : cela nécessite de nombreuses multiplications de matrices et vecteurs. Imaginez une énorme feuille de calcul remplie de nombres que vous multiplierez entre eux tous au même moment.
Created: Sun Dec 28 07:19:09 GMT 2025 - Last Modified: Sun Aug 31 09:56:21 GMT 2025 - 25.4K bytes - Click Count (0) -
docs/en/docs/advanced/events.md
## Use Case { #use-case } Let's start with an example **use case** and then see how to solve it with this. Let's imagine that you have some **machine learning models** that you want to use to handle requests. 🤖Created: Sun Dec 28 07:19:09 GMT 2025 - Last Modified: Wed Dec 17 20:41:43 GMT 2025 - 7.9K bytes - Click Count (0) -
docs/pt/docs/advanced/events.md
## Caso de uso { #use-case } Vamos começar com um exemplo de **caso de uso** e então ver como resolvê-lo com isso. Vamos imaginar que você tem alguns **modelos de machine learning** que deseja usar para lidar com as requisições. 🤖Created: Sun Dec 28 07:19:09 GMT 2025 - Last Modified: Wed Dec 17 20:41:43 GMT 2025 - 8.8K bytes - Click Count (0)