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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

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Product type Paperback
Published in Sep 2017
Publisher Packt
ISBN-13 9781785882104
Length 304 pages
Edition 1st Edition
Languages
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Author (1):
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Joseph Langstaff Whitenack Joseph Langstaff Whitenack
Author Profile Icon Joseph Langstaff Whitenack
Joseph Langstaff Whitenack
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Table of Contents (11) Chapters Close

Preface 1. Gathering and Organizing Data FREE CHAPTER 2. Matrices, Probability, and Statistics 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

Utilizing the simple neural network

Now that we have some neural network training functionality that appears to be working, let's try to utilize this functionality in a more realistic modeling scenario. In particular, let's bring back our favorite classification dataset, the iris flower dataset (utilized in Chapter 5, Classification).

If you remember, when trying to classify iris flowers using this dataset, we are trying to classify them into one of three species (setosa, virginica, or versicolor). As our neural net is expecting matrices of float values, we need to encode the three species into numerical columns. One way to do this is to create a column in our dataset for each species. We will then set that column's values to either 1.0 or 0.0 depending on whether the corresponding row's measurements correspond to that species (1.0) or to another species (0...

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