Deploying TensorFlow Models to a Web Application : Using Flask API, TensorFlowJS, and TensorFlow Serving /

Implement machine learning to realize the power of AI algorithms. Developers and companies often struggle to deploy machine learning models efficiently. One of the main reasons for this is a lack of proper process set up and execution. After getting feedback and comments from his YouTube subscribers...

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Detalles Bibliográficos
Autor principal: Karunanidhi, Vikraman
Autor Corporativo: Safari, an O Reilly Media Company
Formato: Electrónico Video
Idioma:Inglés
Publicado: Apress, 2020.
Edición:1st edition.
Temas:
Streaming video.
Internet videos.
Vidéo en continu.
Vidéos sur Internet.
streaming video.
Internet videos
Streaming video
Electronic videos.
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)

MARC

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520 |a Implement machine learning to realize the power of AI algorithms. Developers and companies often struggle to deploy machine learning models efficiently. One of the main reasons for this is a lack of proper process set up and execution. After getting feedback and comments from his YouTube subscribers, Vikraman has created a system of step-by-step instructions for the process. Using TensorFlow.js, you'll walk through the process of deploying machine learning models in web applications. You'll learn to deploy these models at scale and to work with users' existing hardware such as web cams to accomplish common machine learning tasks. What You Will Learn Deploy machine learning models at scale Save, export, and restore machine learning models Use Flask to work with TensorFlow and Keras models Who This Video Is For Engineers, coders, and researchers who wish to deploy machine learning models in web applications. A basic understanding of TensorFlow, Python, HTML and general machine learning and deep learning algorithms is helpful. 
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