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Sinopsis
Learn the skills necessary to design, build, and deploy applications powered by machine learning (ML). Through the course of this hands-on book, youâ€Â(TM)ll build an example ML-driven application from initial idea to deployed product. Data scientists, software engineers, and product managersâ€Â"including experienced practitioners and novices alikeâ€Â"will learn the tools, best practices, and challenges involved in building a real-world ML application step by step.
Author Emmanuel Ameisen, an experienced data scientist who led an AI education program, demonstrates practical ML concepts using code snippets, illustrations, screenshots, and interviews with industry leaders. Part I teaches you how to plan an ML application and measure success. Part II explains how to build a working ML model. Part III demonstrates ways to improve the model until it fulfills your original vision. Part IV covers deployment and monitoring strategies.
This book will help you:
- Define your product goal and set up a machine learning problem
- Build your first end-to-end pipeline quickly and acquire an initial dataset
- Train and evaluate your ML models and address performance bottlenecks
- Deploy and monitor your models in a production environment
"Sinopsis" puede pertenecer a otra edición de este libro.
Acerca del autor
Emmanuel Ameisen has worked for years as a Data Scientist. He implemented and deployed predictive analytics and machine learning solutions for Local Motion and Zipcar. Recently, Emmanuel has led Insight Data Science's AI program where he oversaw more than a hundred machine learning projects. Emmanuel holds graduate degrees in artificial intelligence, computer engineering, and management from three of France's top schools.
"Sobre este título" puede pertenecer a otra edición de este libro.
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Building Machine Learning Powered Applications: Going from Idea to Product
Publicado por
O'Reilly Media, 2020
ISBN 10: 149204511X
ISBN 13: 9781492045113
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Building Machine Learning Powered Applications: Going from Idea to Product
Publicado por
O'Reilly Media, 2020
ISBN 10: 149204511X
ISBN 13: 9781492045113
Antiguo o usado
Paperback
Librería: ThriftBooks-Dallas, Dallas, TX, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Paperback. Condición: Good. No Jacket. Former library book; Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less. Nº de ref. del artículo: G149204511XI3N10
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Building Machine Learning Powered Applications : Going from Idea to Product
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O'Reilly Media, Incorporated, 2020
ISBN 10: 149204511X
ISBN 13: 9781492045113
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Building Machine Learning Powered Applications : Going from Idea to Product
Publicado por
O'Reilly Media, Incorporated, 2020
ISBN 10: 149204511X
ISBN 13: 9781492045113
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Building Machine Learning Powered Applications
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Building Machine Learning Powe
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Building Machine Learning Powe
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Building Machine Learning Powered Applications
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Building Machine Learning Powered Applications: Going from Idea to Product (Paperback or Softback)
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ISBN 10: 149204511X
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Building Machine Learning Powered Applications
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Paperback. Condición: New. Learn the skills necessary to design, build, and deploy applications powered by machine learning (ML). Through the course of this hands-on book, you'll build an example ML-driven application from initial idea to deployed product. Data scientists, software engineers, and product managers-including experienced practitioners and novices alike-will learn the tools, best practices, and challenges involved in building a real-world ML application step by step.Author Emmanuel Ameisen, an experienced data scientist who led an AI education program, demonstrates practical ML concepts using code snippets, illustrations, screenshots, and interviews with industry leaders. Part I teaches you how to plan an ML application and measure success. Part II explains how to build a working ML model. Part III demonstrates ways to improve the model until it fulfills your original vision. Part IV covers deployment and monitoring strategies.This book will help you:Define your product goal and set up a machine learning problemBuild your first end-to-end pipeline quickly and acquire an initial datasetTrain and evaluate your ML models and address performance bottlenecksDeploy and monitor your models in a production environment. Nº de ref. del artículo: LU-9781492045113
Cantidad disponible: Más de 20 disponibles