Building Machine Learning Powered Applications 在線電子書 pdf 下載 txt下載 epub 下載 mobi 下載 2024


Building Machine Learning Powered Applications

簡體網頁||繁體網頁
Emmanuel Ameisen 作者
O'Reilly Media
譯者
出版日期
250 頁數
USD 59.99 價格
Paperback
叢書系列
9781492045113 圖書編碼

Building Machine Learning Powered Applications 在線電子書 圖書標籤: 計算機  EmmanuelAmeisen  非虛構  機器學習  Product   


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發表於2024-12-24

Building Machine Learning Powered Applications 在線電子書 epub 下載 mobi 下載 pdf 下載 txt 下載 2024

Building Machine Learning Powered Applications 在線電子書 epub 下載 pdf 下載 mobi 下載 txt 下載 2024

Building Machine Learning Powered Applications 在線電子書 pdf 下載 txt下載 epub 下載 mobi 下載 2024



Building Machine Learning Powered Applications 在線電子書 用戶評價

評分

對於從業者來說就是係統性地梳理一下相關知識點 但每一點都過於簡略;對於新手來說知識點又過於龐雜瞭 感覺定位有些模糊

評分

對於從業者來說就是係統性地梳理一下相關知識點 但每一點都過於簡略;對於新手來說知識點又過於龐雜瞭 感覺定位有些模糊

評分

對於從業者來說就是係統性地梳理一下相關知識點 但每一點都過於簡略;對於新手來說知識點又過於龐雜瞭 感覺定位有些模糊

評分

對於從業者來說就是係統性地梳理一下相關知識點 但每一點都過於簡略;對於新手來說知識點又過於龐雜瞭 感覺定位有些模糊

評分

對於從業者來說就是係統性地梳理一下相關知識點 但每一點都過於簡略;對於新手來說知識點又過於龐雜瞭 感覺定位有些模糊

Building Machine Learning Powered Applications 在線電子書 著者簡介

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.


Building Machine Learning Powered Applications 在線電子書 著者簡介


Building Machine Learning Powered Applications 在線電子書 pdf 下載 txt下載 epub 下載 mobi 在線電子書下載

Building Machine Learning Powered Applications 在線電子書 圖書描述

Learn the skills necessary to design, build, and deploy applications powered by machine learning. 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 with little or no ML experience will learn the tools, best practices, and challenges involved in building a real-world ML application step-by-step.

Author Emmanuel Ameisen, who worked as a data scientist at Zipcar and led Insight Data Science’s AI program, demonstrates key ML concepts with code snippets, illustrations, and screenshots from the book’s example application.

The first part of this guide shows you how to plan and measure success for an ML application. Part II shows you how to build a working ML model, and Part III explains how to improve the model until it fulfills your original vision. Part IV covers deployment and monitoring strategies.

This book will help you:

Determine 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 model and address performance bottlenecks

Deploy and monitor models in a production environment

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