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Machine learning with Go : implement regression, classification, clustering, time-series models, neural networks, and more using the Go programming language /
Build simple, maintainable, and easy to deploy machine learning applications. About This Book Build simple, but powerful, machine learning applications that leverage Go's standard library along with popular Go packages. Learn the statistics, algorithms, and techniques needed to successfully imp...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham, UK :
Packt Publishing,
2017.
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| Temas: | |
| Acceso en línea: | Texto completo Texto completo |
MARC
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| 100 | 1 | |a Whitenack, Daniel, |e author. | |
| 245 | 1 | 0 | |a Machine learning with Go : |b implement regression, classification, clustering, time-series models, neural networks, and more using the Go programming language / |c Daniel Whitenack. |
| 264 | 1 | |a Birmingham, UK : |b Packt Publishing, |c 2017. | |
| 300 | |a 1 online resource (1 volume) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 588 | 0 | |a Online resource; title from title page (Safari, viewed October 24, 2017). | |
| 504 | |a Includes bibliographical references. | ||
| 520 | |a Build simple, maintainable, and easy to deploy machine learning applications. About This Book Build simple, but powerful, machine learning applications that leverage Go's standard library along with popular Go packages. Learn the statistics, algorithms, and techniques needed to successfully implement machine learning in Go Understand when and how to integrate certain types of machine learning model in Go applications. Who This Book Is For This book is for Go developers who are familiar with the Go syntax and can develop, build, and run basic Go programs. If you want to explore the field of machine learning and you love Go, then this book is for you! Machine Learning with Go will give readers the practical skills to perform the most common machine learning tasks with Go. Familiarity with some statistics and math topics is necessary. What You Will Learn Learn about data gathering, organization, parsing, and cleaning. Explore matrices, linear algebra, statistics, and probability. See how to evaluate and validate models. Look at regression, classification, clustering. Learn about neural networks and deep learning Utilize times series models and anomaly detection. Get to grip with techniques for deploying and distributing analyses and models. Optimize machine learning workflow techniques In Detail The mission of this book is to turn readers into productive, innovative data analysts who leverage Go to build robust and valuable applications. To this end, the book clearly introduces the technical aspects of building predictive models in Go, but it also helps the reader understand how machine learning workflows are being applied in real-world scenarios. Machine Learning with Go shows readers how to be productive in machine learning while also producing applications that maintain a high level of integrity. It also gives readers patterns to overcome challenges that are often encountered when trying to integrate machine learning in an engineering organization. The readers will begin by gaining a solid understanding of how to gather, organize, and parse real-work data from a variety of sources. Readers will then develop a solid statistical toolkit that will allow them to quickly understand gain intuition about the content of a dataset. Finally, the readers will gain hands-on experience implementing essential machine learning techniques (regression, classification, clustering, and so on) with the relevant Go packages. Finally, the reader will have a solid m ... | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 590 | |a eBooks on EBSCOhost |b EBSCO eBook Subscription Academic Collection - Worldwide | ||
| 650 | 0 | |a Go (Computer program language) | |
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Application software |x Development. | |
| 650 | 0 | |a Big data. | |
| 650 | 6 | |a Go (Langage de programmation) | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 6 | |a Logiciels d'application |x Développement. | |
| 650 | 6 | |a Données volumineuses. | |
| 650 | 7 | |a COMPUTERS |x Natural Language Processing. |2 bisacsh | |
| 650 | 7 | |a COMPUTERS |x Intelligence (AI) & Semantics. |2 bisacsh | |
| 650 | 7 | |a COMPUTERS |x Neural Networks. |2 bisacsh | |
| 650 | 7 | |a Application software |x Development. |2 fast |0 (OCoLC)fst00811707 | |
| 650 | 7 | |a Big data. |2 fast |0 (OCoLC)fst01892965 | |
| 650 | 7 | |a Go (Computer program language) |2 fast |0 (OCoLC)fst01893916 | |
| 650 | 7 | |a Machine learning. |2 fast |0 (OCoLC)fst01004795 | |
| 776 | 0 | 8 | |i Print version: |a Whitenack, Daniel. |t Machine learning with Go : implement regression, classification, clustering, time-series models, neural networks, and more using the Go programming language. |d Birmingham, England ; Mumbai, India : Packt Publishing, 2017 |z 9781785882104 |
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