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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" }
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docs/pt/docs/advanced/events.md
## Caso de uso Vamos iniciar 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. 🤖
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docs/en/docs/tutorial/path-params.md
!!! tip If you are wondering, "AlexNet", "ResNet", and "LeNet" are just names of Machine Learning <abbr title="Technically, Deep Learning model architectures">models</abbr>. ### Declare a *path parameter* Then create a *path parameter* with a type annotation using the enum class you created (`ModelName`):
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docs/es/docs/tutorial/path-params.md
!!! tip "Consejo" Si lo estás dudando, "AlexNet", "ResNet", y "LeNet" son solo nombres de <abbr title="Técnicamente, arquitecturas de modelos de Deep Learning">modelos</abbr> de Machine Learning. ### Declara un *parámetro de path* Luego, crea un *parámetro de path* con anotaciones de tipos usando la clase enum que creaste (`ModelName`): ```Python hl_lines="16"
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docs/pt/docs/tutorial/path-params.md
!!! tip "Dica" Se você está se perguntando, "AlexNet", "ResNet", e "LeNet" são apenas nomes de <abbr title="técnicamente, modelos de arquitetura de Deep Learning">modelos</abbr> de Machine Learning (aprendizado de máquina). ### Declare um *parâmetro de rota* Logo, crie um *parâmetro de rota* com anotações de tipo usando a classe enum que você criou (`ModelName`): ```Python hl_lines="16"
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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.
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.github/workflows/trusted_partners.js
const lowercased_title = (title || '').toLowerCase(); const onednn_assignees = ['penpornk']; if (lowercased_title.includes('onednn')) assignees = onednn_assignees; const intel_windows_assignees = ['nitins17', 'learning-to-play']; if (lowercased_title.includes('intel') && lowercased_title.includes('windows') && domain.includes('intel.com')) assignees = intel_windows_assignees;
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docs/en/docs/history-design-future.md
Here's a little bit of that history. ## Alternatives I have been creating APIs with complex requirements for several years (Machine Learning, distributed systems, asynchronous jobs, NoSQL databases, etc), leading several teams of developers. As part of that, I needed to investigate, test and use many alternatives. The history of **FastAPI** is in great part the history of its predecessors.
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tensorflow/BUILD
# TODO(b/173549186): Move Google-internal TF code out of learning/brain package_group( name = "internal", packages = [ "//devtools/python/indexer/...", "//learning/brain/keras/...", "//learning/brain/mlir/...", "//learning/brain/tfrt/...", "//learning/lib/ami/simple_ml/...", "//learning/pathways/...", "//learning/serving/contrib/tfrt/mlir/canonical_ops/...",
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ci/official/utilities/generate_index_html.sh
<li><a href="http://cs/f:devtools/kokoro/config/prod/$KOKORO_JOB_NAME">Codesearch - job definition</a></li> <li><a href="http://cs/f:learning/brain/testing/kokoro/$(echo "$KOKORO_JOB_NAME" | sed 's!tensorflow/!!g')">Codesearch - build definition & scripts</a></li> <li><a href="http://cs/$KOKORO_JOB_NAME">Codesearch - All references to this job</a></li> </ul>
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