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Machine Learning With Go

You're reading from Machine Learning With Go Implement Regression, Classification, Clustering, Time-series Models, Neural Networks, and More using the Go Programming Language

Product type Paperback

Published in Sep 2017

Publisher Packt

ISBN-13 9781785882104

Length 304 pages

Edition 1st Edition

Languages

Go

Concepts

Machine Learning

Author (1):

Joseph Langstaff Whitenack

View More author details

Table of Contents (11) Chapters

Preface

1. Gathering and Organizing Data

2. Matrices, Probability, and Statistics FREE CHAPTER

3. Evaluation and Validation

4. Regression

5. Classification

6. Clustering

7. Time Series and Anomaly Detection

8. Neural Networks and Deep Learning

9. Deploying and Distributing Analyses and Models

10. Algorithms/Techniques Related to Machine Learning

Summary

This introduction to matrices, linear algebra, statistics, and probability in Go has given us a set of tools to understand, structure, and operate on data. This set of tools will be used throughout the book as we work on a diverse set of problems, and these tools could be used in a variety of contexts outside of machine learning. However, in the next chapter, we will discuss some ideas and techniques that will be extremely important in the machine learning context, specifically, evaluation and validation.

You have been reading a chapter from

Machine Learning With Go

Published in: Sep 2017

Publisher: Packt

ISBN-13: 9781785882104

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Authors (1)

Joseph Langstaff Whitenack

Joseph Langstaff 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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