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001433057 001__ 1433057
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001433057 020__ $$a9781484265437$$q(electronic bk.)
001433057 020__ $$a1484265432$$q(electronic bk.)
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001433057 0247_ $$a10.1007/978-1-4842-6543-7$$2doi
001433057 035__ $$aSP(OCoLC)1228034113
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001433057 08204 $$a006.3/1$$223
001433057 08204 $$a004.165$$223
001433057 1001_ $$aMukherjee, Sudipta.
001433057 24510 $$aML.NET revealed :$$bsimple tools for applying machine learning to your applications /$$cSudipta Mukherjee.
001433057 260__ $$aBerkeley, CA :$$bApress,$$c2021.
001433057 300__ $$a1 online resource (185 pages)
001433057 336__ $$atext$$btxt$$2rdacontent
001433057 337__ $$acomputer$$bc$$2rdamedia
001433057 338__ $$aonline resource$$bcr$$2rdacarrier
001433057 347__ $$atext file
001433057 347__ $$bPDF
001433057 500__ $$aIncludes index.
001433057 5050_ $$aChapter 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.
001433057 506__ $$aAccess limited to authorized users.
001433057 520__ $$aGet 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
001433057 588__ $$aDescription based on print version record.
001433057 63000 $$aMicrosoft .NET Framework.
001433057 650_0 $$aMachine learning.
001433057 650_6 $$aApprentissage automatique.
001433057 655_0 $$aElectronic books.
001433057 77608 $$iPrint version:$$aMukherjee, Sudipta.$$tML. NET Revealed : Simple Tools for Applying Machine Learning to Your Applications.$$dBerkeley, CA : Apress L.P., ©2021$$z9781484265420
001433057 852__ $$bebk
001433057 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-1-4842-6543-7$$zOnline Access$$91397441.1
001433057 909CO $$ooai:library.usi.edu:1433057$$pGLOBAL_SET
001433057 980__ $$aBIB
001433057 980__ $$aEBOOK
001433057 982__ $$aEbook
001433057 983__ $$aOnline
001433057 994__ $$a92$$bISE