소장자료
| LDR | 03012cam a2200000 a | ||
| 001 | 0100461821▲ | ||
| 003 | OCoLC▲ | ||
| 005 | 20200518135354▲ | ||
| 007 | ta ▲ | ||
| 008 | 190630t2020 caua 001 0 eng c▲ | ||
| 020 | ▼a149204511X (pbk.)▲ | ||
| 020 | ▼a9781492045113 (pbk.)▲ | ||
| 035 | ▼a(OCoLC)1106176581▲ | ||
| 040 | ▼aYDX▼beng▼cYDX▼dBDX▼dGK8▼dDAD▼dOCLCF▼dUKMGB▼dYDXIT▼d221016▲ | ||
| 082 | 0 | 4 | ▼a006.31▼223▲ |
| 090 | ▼a006.31▼bA498b▲ | ||
| 100 | 1 | ▼aAmeisen, Emmanuel.▲ | |
| 245 | 1 | 0 | ▼aBuilding machine learning powered applications :▼bgoing from idea to product /▼cEmmanuel Ameisen.▲ |
| 260 | ▼aSebastopol, CA :▼bO'Reilly Media, Inc.,▼c2020.▲ | ||
| 300 | ▼axvii, 238 p. :▼bill. ;▼c24 cm.▲ | ||
| 500 | ▼aIncludes index.▲ | ||
| 505 | 0 | ▼aFrom product goal to ML framing -- Create a plan -- Build your firest end-to-end pipeline -- Acquire an initial dataset -- Train and evaluate your model -- Debug your ML problems -- Using classifiers for writing recommendations -- Considerations when deploying models -- Choose your deployment option -- Build safeguards for models -- Monitor and update models.▲ | |
| 520 | ▼aLearn 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.▲ | ||
| 650 | 0 | ▼aMachine learning.▲ | |
| 650 | 0 | ▼aApplication software▼xDevelopment.▲ |
Building machine learning powered applications :going from idea to product
자료유형
국외단행본
서명/책임사항
Building machine learning powered applications : going from idea to product / Emmanuel Ameisen.
발행사항
Sebastopol, CA : O'Reilly Media, Inc. , 2020.
형태사항
xvii, 238 p. : ill. ; 24 cm.
일반주기
Includes index.
내용주기
From product goal to ML framing -- Create a plan -- Build your firest end-to-end pipeline -- Acquire an initial dataset -- Train and evaluate your model -- Debug your ML problems -- Using classifiers for writing recommendations -- Considerations when deploying models -- Choose your deployment option -- Build safeguards for models -- Monitor and update models.
요약주기
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.
ISBN
149204511X (pbk.) 9781492045113 (pbk.)
청구기호
006.31 A498b
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