Janani Selvaraj

Daniel Whitenack

Infuse an extra layer of intelligence into your Go applications with machine learning and AI

Key Features:

Build simple, maintainable, and easy to deploy machine learning applications with popular Go packagesLearn the statistics, algorithms, and techniques to implement machine learningOvercome the common challenges faced while deploying and scaling the machine learning workflows

Book Description:

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

What you will learnBecome well versed with data processing, parsing, and cleaning using Go packagesLearn to gather data from various sources and in various real-world formatsPerform regression, classification, and image processing with neural networksEvaluate and detect anomalies in a time series modelUnderstand common deep learning architectures to learn how each model is builtLearn how to optimize, build, and scale machine learning workflowsDiscover the best practices for machine learning model tuning for successful deployments

Who this book is for:

This book is primarily for Go programmers who want to become a machine learning engineer and to build a solid machine learning mindset along with a good hold on Go packages. This is also useful for data analysts, data engineers, machine learning users who want to run their machine learning experiments using the Go ecosystem. Prior understanding of linear algebra is required to benefit from this book

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). 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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