國立虎尾科技大學 |

Building Intelligent Systems = A Guide to Machine Learning Engineering /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Building Intelligent Systems/ by Geoff Hulten.
其他題名:	A Guide to Machine Learning Engineering /
作者:	Hulten, Geoff.
面頁冊數:	XXVI, 339 p. 19 illus.online resource. :
Contained By:	Springer Nature eBook
標題:	Artificial intelligence. -
電子資源:	https://doi.org/10.1007/978-1-4842-3432-7
ISBN:	9781484234327

Building Intelligent Systems = A Guide to Machine Learning Engineering /
Hulten, Geoff.

Building Intelligent SystemsA Guide to Machine Learning Engineering /[electronic resource] :by Geoff Hulten. - 1st ed. 2018. - XXVI, 339 p. 19 illus.online resource.

Part 1: Approaching an Intelligent System Project -- Chapter 1: Introducing Intelligent Systems -- Chapter 2: Knowing When to Use Intelligent Systems -- Chapter 3: A Brief Refresher on Working with Data -- Chapter 4: Defining the Intelligent System's Goals -- Part 2: Intelligent Experiences -- Chapter 5: The Components of Intelligent Experiences -- Chapter 6: Why Creating Intelligence Experiences Is Hard -- Chapter 7: Balancing Intelligent Experiences -- Chapter 8: Modes of Intelligent Interaction -- Chapter 9: Getting Data from Experience -- Chapter 10: Verifying Intelligent Experiences -- Part 3: Implementing Intelligence -- Chapter 11: The Components of an Intelligence Implementation -- Chapter 12: The Intelligence Runtime -- Chapter 13: Where Intelligence Lives -- Chapter 14: Intelligence Management -- Chapter 15: Intelligent Telemetry -- Part 4: Creating Intelligence -- Chapter 16: Overview of Intelligence -- Chapter 17: Representing Intelligence -- Chapter 18: The Intelligence Creation Process -- Chapter 19: Evaluating Intelligence -- Chapter 20: Machine Learning Intelligence -- Chapter 21: Organizing Intelligence -- Part 5: Orchestrating Intelligent Systems -- Chapter 22: Overview of Intelligence Orchestration -- Chapter 23: The Intelligence Orchestration Environment -- Chapter 24: Dealing with Mistakes -- Chapter 25: Adversaries and Abuse -- Chapter 26: Approaching Your Own Intelligent System -- .

Produce a fully functioning Intelligent System that leverages machine learning and data from user interactions to improve over time and achieve success. This book teaches you how to build an Intelligent System from end to end and leverage machine learning in practice. You will understand how to apply your existing skills in software engineering, data science, machine learning, management, and program management to produce working systems. Building Intelligent Systems is based on more than a decade of experience building Internet-scale Intelligent Systems that have hundreds of millions of user interactions per day in some of the largest and most important software systems in the world. What You’ll Learn: Understand the concept of an Intelligent System: What it is good for, when you need one, and how to set it up for success Design an intelligent user experience: Produce data to help make the Intelligent System better over time Implement an Intelligent System: Execute, manage, and measure Intelligent Systems in practice Create intelligence: Use different approaches, including machine learning Orchestrate an Intelligent System: Bring the parts together throughout its life cycle and achieve the impact you want.

ISBN: 9781484234327

Standard No.: 10.1007/978-1-4842-3432-7doiSubjects--Topical Terms:

559380
Artificial intelligence.

LC Class. No.: Q334-342

Dewey Class. No.: 006.3

Building Intelligent Systems = A Guide to Machine Learning Engineering /
LDR:04007nam a22003975i 4500 001 994300
003 DE-He213
005 20200706063301.0
007 cr nn 008mamaa
008 201225s2018 xxu| s |||| 0|eng d
020 $a 9781484234327 $9 978-1-4842-3432-7
024 7 $a 10.1007/978-1-4842-3432-7 $2 doi
035 $a 978-1-4842-3432-7
050 4 $a Q334-342
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.3 $2 23
100 1 $a Hulten, Geoff. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1203118
245 1 0 $a Building Intelligent Systems $h [electronic resource] : $b A Guide to Machine Learning Engineering / $c by Geoff Hulten.
250 $a 1st ed. 2018.
264 1 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2018.
300 $a XXVI, 339 p. 19 illus. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
505 0 $a Part 1: Approaching an Intelligent System Project -- Chapter 1: Introducing Intelligent Systems -- Chapter 2: Knowing When to Use Intelligent Systems -- Chapter 3: A Brief Refresher on Working with Data -- Chapter 4: Defining the Intelligent System's Goals -- Part 2: Intelligent Experiences -- Chapter 5: The Components of Intelligent Experiences -- Chapter 6: Why Creating Intelligence Experiences Is Hard -- Chapter 7: Balancing Intelligent Experiences -- Chapter 8: Modes of Intelligent Interaction -- Chapter 9: Getting Data from Experience -- Chapter 10: Verifying Intelligent Experiences -- Part 3: Implementing Intelligence -- Chapter 11: The Components of an Intelligence Implementation -- Chapter 12: The Intelligence Runtime -- Chapter 13: Where Intelligence Lives -- Chapter 14: Intelligence Management -- Chapter 15: Intelligent Telemetry -- Part 4: Creating Intelligence -- Chapter 16: Overview of Intelligence -- Chapter 17: Representing Intelligence -- Chapter 18: The Intelligence Creation Process -- Chapter 19: Evaluating Intelligence -- Chapter 20: Machine Learning Intelligence -- Chapter 21: Organizing Intelligence -- Part 5: Orchestrating Intelligent Systems -- Chapter 22: Overview of Intelligence Orchestration -- Chapter 23: The Intelligence Orchestration Environment -- Chapter 24: Dealing with Mistakes -- Chapter 25: Adversaries and Abuse -- Chapter 26: Approaching Your Own Intelligent System -- .
520 $a Produce a fully functioning Intelligent System that leverages machine learning and data from user interactions to improve over time and achieve success. This book teaches you how to build an Intelligent System from end to end and leverage machine learning in practice. You will understand how to apply your existing skills in software engineering, data science, machine learning, management, and program management to produce working systems. Building Intelligent Systems is based on more than a decade of experience building Internet-scale Intelligent Systems that have hundreds of millions of user interactions per day in some of the largest and most important software systems in the world. What You’ll Learn: Understand the concept of an Intelligent System: What it is good for, when you need one, and how to set it up for success Design an intelligent user experience: Produce data to help make the Intelligent System better over time Implement an Intelligent System: Execute, manage, and measure Intelligent Systems in practice Create intelligence: Use different approaches, including machine learning Orchestrate an Intelligent System: Bring the parts together throughout its life cycle and achieve the impact you want.
650 0 $a Artificial intelligence. $3 559380
650 0 $a Big data. $3 981821
650 1 4 $a Artificial Intelligence. $3 646849
650 2 4 $a Big Data. $3 1017136
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9781484234310
776 0 8 $i Printed edition: $z 9781484234334
776 0 8 $i Printed edition: $z 9781484246368
856 4 0 $u https://doi.org/10.1007/978-1-4842-3432-7
912 $a ZDB-2-CWD
912 $a ZDB-2-SXPC
950 $a Professional and Applied Computing (SpringerNature-12059)
950 $a Professional and Applied Computing (R0) (SpringerNature-43716)