Machine Learning With Go: Implement Regression, Classification, Clustering, Time-series Models, Neural Networks, and More using the Go Programming Language (English Edition)

  • 304 pages
  • Anglais
  • Format Kindle
Machine Learning With Go: Implement Regression, Classification, Clustering, Time-series Models, Neural Networks, and More using the Go Programming Language (English Edition)

╟ Best new ⛅ Machine Learning With Go: Implement Regression, Classification, Clustering, Time-series Models, Neural Networks, and More using the Go Programming Language (English Edition) ▏ Book Author Daniel Whitenack ◝ Key FeaturesBuild 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 GoUnderstand when and how to integrate certain types of machine learning model in Go applications.Book DescriptionThe 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 machine learning mindset and a powerful Go toolkit of techniques, packages, and example implementations.What you will learnLearn 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 learningUtilize times series models and anomaly detection.Get to grip with techniques for deploying and distributing analyses and models.Optimize machine learning workflow techniquesAbout the AuthorYogesh Sajanikar dwhitena , PhD, is a trained data scientist working with Pachyderm pachydermIO Daniel develops innovative, distributed data pipelines that include predictive models, data visualizations, statistical analyses, and He has spoken at conferences around the world GopherCon, JuliaCon, PyCon, ODSC, Spark Summit, and , teaches data science engineering at Purdue University LifeAtPurdue , and, with Ardan Labs ardanlabs , maintains the Go kernel for Jupyter, and is actively helping to organize contributions to various open source data science projects.Table of ContentsGathering and Organizing Data Matrices, Probability, and Statistics Evaluation and Validation Regression Classification Clustering Time Series and Anomaly Detection Neural Networks and Deep Learning Deploying and distributing Analyses and Models Appendix Algorithms Techniques Related to MLDaniel Whitenack dwhitena , PhD, is a trained data scientist working with Pachyderm pachydermIO Daniel develops innovative, distributed data pipelines that include predictive models, data visualizations, statistical analyses, and He has spoken at conferences around the world GopherCon, JuliaCon, PyCon, ODSC, Spark Summit, and , teaches data science engineering at Purdue University LifeAtPurdue , and, with Ardan Labs ardanlabs , maintains the Go kernel for Jupyter, and is actively helping to organize contributions to various open source data science projects. 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