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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

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Table of Contents (17) Chapters

Title Page

Credits

About the Author

About the Reviewers

www.PacktPub.com

Customer Feedback

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

References

Evaluation:

Essay on overfitting: http://scott.fortmann-roe.com/docs/MeasuringError.html
Essay on bias-variance trade-off: http://scott.fortmann-roe.com/docs/BiasVariance.html
Comparison of categorical evaluation metrics: https://en.wikipedia.org/wiki/Precision_and_recall
gonum.org/v1/gonum/stat docs: https://godoc.org/gonum.org/v1/gonum/stat
github.com/sjwhitworth/golearn/evaluation docs: https://godoc.org/github.com/sjwhitworth/golearn/evaluation

Validation:

github.com/kniren/gota/dataframe docs: https://godoc.org/github.com/kniren/gota/dataframe
github.com/sjwhitworth/golearn/evaluation docs: https://godoc.org/github.com/sjwhitworth/golearn/evaluation

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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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