Produs

Building Machine Learning Powered Applications - Emmanuel Ameisen

Name: Building Machine Learning Powered Applications - Emmanuel Ameisen
Brand: Emmanuel Ameisen
Price: 368.23 RON
Availability: InStock

Vânzător

libris.ro

Cumpără

Pret

368.23 Lei 409.14 Lei

Categorie (vânzător)

Computers

Marca

Emmanuel Ameisen

Produs actualizat în urmă cu 11 luni

Descriere YEO:

Building Machine Learning Powered Applications - Disponibil la libris.ro

Pe YEO găsești Building Machine Learning Powered Applications de la Emmanuel Ameisen, în categoria Computers.

Indiferent de nevoile tale, Building Machine Learning Powered Applications - Emmanuel Ameisen din categoria Computers îți poate aduce un echilibru perfect între calitate și preț, cu avantaje practice și moderne.

Preț: 368.23 Lei

Caracteristicile produsului Building Machine Learning Powered Applications

Brand: Emmanuel Ameisen
Categoria: Computers
Magazin: libris.ro
Ultima actualizare: 05-06-2025 16:21:01

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Descriere magazin:
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 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

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