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CITATION.cff
cff-version: 1.2.0 message: "If you use TensorFlow in your research, please cite it using these metadata. Software is available from tensorflow.org." title: TensorFlow, Large-scale machine learning on heterogeneous systems
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docs/en/docs/advanced/index.md
Some course providers ✨ [**sponsor FastAPI**](../help-fastapi.md#sponsor-the-author){.internal-link target=_blank} ✨, this ensures the continued and healthy **development** of FastAPI and its **ecosystem**.
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docs/en/docs/advanced/events.md
## 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. 🤖
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SECURITY.md
## TensorFlow models are programs TensorFlow [**models**](https://developers.google.com/machine-learning/glossary/#model) (to use a term commonly used by machine learning practitioners) are expressed as programs that TensorFlow executes. TensorFlow programs are encoded as computation [**graphs**](https://developers.google.com/machine-learning/glossary/#graph). Since models are practically programs that TensorFlow executes, using untrusted
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docs_src/path_params/tutorial005.py
app = FastAPI() @app.get("/models/{model_name}") async def get_model(model_name: ModelName): if model_name is ModelName.alexnet: return {"model_name": model_name, "message": "Deep Learning FTW!"} if model_name.value == "lenet": return {"model_name": model_name, "message": "LeCNN all the images"}
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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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