Machine Learning With Go - Second Edition

By Daniel Whitenack , Janani Selvar
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  1. Gathering and Organizing Data

About this book

This updated edition of the popular Machine Learning With Go shows you how to overcome the common challenges of integrating analysis and machine learning code within an existing engineering organization.

Machine Learning With Go, Second Edition, will begin by helping you gain an understanding of how to gather, organize, and parse real-world data from a variety of sources. The book also provides absolute coverage in developing groundbreaking machine learning pipelines including predictive models, data visualizations, and statistical techniques. Up next, you will learn the thorough utilization of Golang libraries including golearn, gorgonia, gosl, hector, and mat64. You will discover the various TensorFlow capabilities, along with building simple neural networks and integrating them into machine learning models. You will also gain hands-on experience implementing essential machine learning techniques such as regression, classification, and clustering with the relevant Go packages. Furthermore, you will deep dive into the various Go tools that help you build deep neural networks. Lastly, you will become well versed with best practices for machine learning model tuning and optimization.

By the end of the book, you will have a solid machine learning mindset and a powerful Go toolkit of techniques, packages, and example implementations

Publication date:
April 2019

About the Authors

  • Daniel Whitenack

    Daniel Whitenack is a trained PhD data scientist with over 10 years' experience working on data-intensive applications in industry and academia. Recently, Daniel has focused his development efforts on open source projects related to running machine learning (ML) and artificial intelligence (AI) in cloud-native infrastructure (Kubernetes, for instance), maintaining reproducibility and provenance for complex data pipelines, and implementing ML/AI methods in new languages such as Go. Daniel co-hosts the Practical AI podcast, teaches data science/engineering at Ardan Labs and Purdue University, and has spoken at conferences around the world (including ODSC, PyCon, DataEngConf, QCon, GopherCon, Spark Summit, and Applied ML Days, among others).

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  • Janani Selvar

    Janani Selvaraj works as a senior research and analytics consultant for a start-up in Trichy, Tamil Nadu. She is a mathematics graduate with PhD in environmental management. Her current interests include data wrangling and visualization, machine learning, and geospatial modeling. She currently trains students in data science and works as a consultant on several data-driven projects in a variety of domains. She is an R programming expert and founder of the R-Ladies Trichy group, a group that promotes gender diversity. She has served as a reviewer for Go-Machine learning Projects book.

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