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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

Evaluating algorithms

There are many dimensions upon which we can evaluate the algorithms. This section explores how to evaluate algorithms.

Assuming we want to have fast face detection—which algorithm would be better?

The only way to understand the performance of an algorithm is to measure it. Thankfully Go comes with benchmarking built in. That is what we are about to do.

To build benchmarks we must be very careful about what we're benchmarking. In this case, we want to benchmark the performance of the detection algorithm. This means comparing classifier.DetectMultiScale versus, pigoClass.RunCascade and pigoClass.ClusterDetections.

Also, we have to compare apples to apples—it would be unfair if we compare one algorithm with a 3840 x 2160 image and the other algorithm with a 640 x 480 image. There are simply more pixels in the former compared to the latter...

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