ML.NET Revealed
Kurzinformation
inkl. MwSt. Versandinformationen
Artikel zZt. nicht lieferbar
Artikel zZt. nicht lieferbar

Beschreibung
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 LearnCreate 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 Who This Book Is For Developers who want to learn how to use and apply machine learning to enrich their applications von Mukherjee, Sudipta
Produktdetails
So garantieren wir Dir zu jeder Zeit Premiumqualität.
Über den Autor
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.
- paperback
- 111 Seiten
- Erschienen 1997
- Paper Tiger
- hardcover
- 365 Seiten
- Erschienen 2014
- Forge
- MP3 -
- Erschienen 2019
- Random House Audio
- Hardcover
- 128 Seiten
- Erschienen 2000
- Faber & Faber
- Kartoniert
- 236 Seiten
- Erschienen 2019
- Panini Verlags GmbH
- Kartoniert
- 160 Seiten
- Erschienen 2013
- Marvel
- paperback
- 208 Seiten
- Erschienen 2007
- Marvel
- Hardcover
- 256 Seiten
- Erschienen 2022
- Soft Skull
- comic
- 160 Seiten
- Erschienen 2007
- Marvel
- paperback
- 216 Seiten
- Erschienen 2007
- Marvel
- Hardcover
- 194 Seiten
- Erschienen 2000
- HarperCollins
- Taschenbuch
- 592 Seiten
- Del Rey
- Gebunden
- 164 Seiten
- Erschienen 2020
- Panini Verlags GmbH
- mass_market
- 304 Seiten
- Erschienen 2018
- Night Shade
- Gebunden
- 504 Seiten
- Erschienen 2021
- Panini Verlags GmbH
- Hardcover
- 172 Seiten
- Erschienen 2023
- Celtic Sea, LLC
- Taschenbuch
- 592 Seiten
- Erschienen 2015
- Baen
- Taschenbuch
- 410 Seiten
- Erschienen 2022
- Severn River Publishing
- paperback
- 448 Seiten
- Erschienen 1996
- Bantam




