國立虎尾科技大學 |

ML.NET Revealed = Simple Tools for Applying Machine Learning to Your Applications /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	ML.NET Revealed/ by Sudipta Mukherjee.
其他題名:	Simple Tools for Applying Machine Learning to Your Applications /
作者:	Mukherjee, Sudipta.
面頁冊數:	XVIII, 174 p. 160 illus.online resource. :
Contained By:	Springer Nature eBook
標題:	Machine Learning. -
電子資源:	https://doi.org/10.1007/978-1-4842-6543-7
ISBN:	9781484265437

ML.NET Revealed = Simple Tools for Applying Machine Learning to Your Applications /
Mukherjee, Sudipta.

ML.NET RevealedSimple Tools for Applying Machine Learning to Your Applications /[electronic resource] :by Sudipta Mukherjee. - 1st ed. 2021. - XVIII, 174 p. 160 illus.online resource.

Chapter 1: Meet ML.NET -- Chapter 2: The Pipeline -- Chapter 3: Handling Data -- Chapter 4: Regressions -- Chapter 5: Classifications -- Chapter 6: Clustering -- Chapter 7: Sentiment Analysis -- Chapter 8: Product Recommendation -- Chapter 9: Anomaly Detection -- Chapter 10: Object Detection.

Get introduced to ML.NET, a new open source, cross-platform machine learning framework from Microsoft that is intended to democratize machine learning and enable as many developers as possible. Dive in to learn how ML.NET is designed to encapsulate complex algorithms, making it easy to consume them in many application settings without having to think about the internal details. You will learn about the features that do the necessary “plumbing” that is required in a variety of machine learning problems, freeing up your time to focus on your applications. You will understand that while the infrastructure pieces may at first appear to be disconnected and haphazard, they are not. Developers who are curious about trying machine learning, yet are shying away from it due to its perceived complexity, will benefit from this book. This introductory guide will help you make sense of it all and inspire you to try out scenarios and code samples that can be used in many real-world situations. What You Will Learn Create a machine learning model using only the C# language Build confidence in your understanding of machine learning algorithms Painlessly implement algorithms Begin using the ML.NET library software Recognize the many opportunities to utilize ML.NET to your advantage Apply and reuse code samples from the book Utilize the bonus algorithm selection quick references available online This book is for developers who want to learn how to use and apply machine learning to enrich their applications. Sudipta Mukherjee is an electronics engineer by education and a computer scientist by profession. He holds a degree in electronics and communication engineering. He is passionate about data structure, algorithms, text processing, natural language processing tools development, programming languages, and machine learning. He is the author of several technical books. He has presented at @FuConf and other developer events, and he lives in Bangalore with his wife and son.

ISBN: 9781484265437

Standard No.: 10.1007/978-1-4842-6543-7doiSubjects--Topical Terms:

1137723
Machine Learning.

LC Class. No.: QA76.76.M52

Dewey Class. No.: 004.165

ML.NET Revealed = Simple Tools for Applying Machine Learning to Your Applications /
LDR:03607nam a22003855i 4500 001 1050834
003 DE-He213
005 20210621164231.0
007 cr nn 008mamaa
008 220103s2021 xxu| s |||| 0|eng d
020 $a 9781484265437 $9 978-1-4842-6543-7
024 7 $a 10.1007/978-1-4842-6543-7 $2 doi
035 $a 978-1-4842-6543-7
050 4 $a QA76.76.M52
072 7 $a UMP $2 bicssc
072 7 $a COM051380 $2 bisacsh
072 7 $a UMP $2 thema
082 0 4 $a 004.165 $2 23
100 1 $a Mukherjee, Sudipta. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1116626
245 1 0 $a ML.NET Revealed $h [electronic resource] : $b Simple Tools for Applying Machine Learning to Your Applications / $c by Sudipta Mukherjee.
250 $a 1st ed. 2021.
264 1 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2021.
300 $a XVIII, 174 p. 160 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 Chapter 1: Meet ML.NET -- Chapter 2: The Pipeline -- Chapter 3: Handling Data -- Chapter 4: Regressions -- Chapter 5: Classifications -- Chapter 6: Clustering -- Chapter 7: Sentiment Analysis -- Chapter 8: Product Recommendation -- Chapter 9: Anomaly Detection -- Chapter 10: Object Detection.
520 $a Get introduced to ML.NET, a new open source, cross-platform machine learning framework from Microsoft that is intended to democratize machine learning and enable as many developers as possible. Dive in to learn how ML.NET is designed to encapsulate complex algorithms, making it easy to consume them in many application settings without having to think about the internal details. You will learn about the features that do the necessary “plumbing” that is required in a variety of machine learning problems, freeing up your time to focus on your applications. You will understand that while the infrastructure pieces may at first appear to be disconnected and haphazard, they are not. Developers who are curious about trying machine learning, yet are shying away from it due to its perceived complexity, will benefit from this book. This introductory guide will help you make sense of it all and inspire you to try out scenarios and code samples that can be used in many real-world situations. What You Will Learn Create a machine learning model using only the C# language Build confidence in your understanding of machine learning algorithms Painlessly implement algorithms Begin using the ML.NET library software Recognize the many opportunities to utilize ML.NET to your advantage Apply and reuse code samples from the book Utilize the bonus algorithm selection quick references available online This book is for developers who want to learn how to use and apply machine learning to enrich their applications. Sudipta Mukherjee is an electronics engineer by education and a computer scientist by profession. He holds a degree in electronics and communication engineering. He is passionate about data structure, algorithms, text processing, natural language processing tools development, programming languages, and machine learning. He is the author of several technical books. He has presented at @FuConf and other developer events, and he lives in Bangalore with his wife and son.
650 2 4 $a Machine Learning. $3 1137723
650 1 4 $a Microsoft and .NET. $3 1114109
650 0 $a Machine learning. $3 561253
650 0 $a Microsoft .NET Framework. $3 565417
650 0 $a Microsoft software. $3 1253736
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9781484265420
776 0 8 $i Printed edition: $z 9781484265444
856 4 0 $u https://doi.org/10.1007/978-1-4842-6543-7
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)