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Go Machine Learning Projects

You're reading from   Go Machine Learning Projects Eight projects demonstrating end-to-end machine learning and predictive analytics applications in Go

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Product type Paperback
Published in Nov 2018
Publisher Packt
ISBN-13 9781788993401
Length 348 pages
Edition 1st Edition
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Author (1):
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Xuanyi Chew Xuanyi Chew
Author Profile Icon Xuanyi Chew
Xuanyi Chew
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Table of Contents (12) Chapters Close

Preface 1. How to Solve All Machine Learning Problems 2. Linear Regression - House Price Prediction FREE CHAPTER 3. Classification - Spam Email Detection 4. Decomposing CO2 Trends Using Time Series Analysis 5. Clean Up Your Personal Twitter Timeline by Clustering Tweets 6. Neural Networks - MNIST Handwriting Recognition 7. Convolutional Neural Networks - MNIST Handwriting Recognition 8. Basic Facial Detection 9. Hot Dog or Not Hot Dog - Using External Services 10. What's Next? 11. Other Books You May Enjoy

Implementating the classifier

In the earlier parts of the chapter, we sketched out a dummy Classifier type that does nothing. Let's make it do something now:

type Classifier struct {
corpus *corpus.Corpus

tfidfs [MAXCLASS]*tfidf.TFIDF
totals [MAXCLASS]float64

ready bool
sync.Mutex
}

Here, there are introductions to a few things. Let's walk them through one by one:

  • We'll start with the corpus.Corpus type.
  • This is a type imported from the corpus package, which is a subpackage of the NLP library for Go, lingo.
  • To install lingo, simply run go get -u github.com/chewxy/lingo/....
  • To use the corpus package, simply import it like so: import "github.com/chewxy/lingo/corpus".
Bear in mind that in the near future, the package will change to github.com/go-nlp/lingo. If you are reading this after January 2019, use the new address.

A corpus.Corpus object simply...

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