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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

K-means

K-means is a method of clustering data. The problem is posed as this—given a dataset of N items, we wish to partition the data into K groups. How do you do so?

Allow me to take a side bar and explore the wonderful world of coordinates. No, no, don't run! It's very visual.

Which line is longer? How do you know?

You know which line is longer because you can measure each line from points a, b, c, and d. Now, let's try something different:

Which dot is closest to X? How do you know?

You know because again, you can measure the distance between the dots. And now, for our final exercise:

Consider the distance between the following:

  • A and X
  • A and Y
  • A and Z
  • B and X
  • B and Y
  • B and Z
  • C and X
  • C and Y
  • C and Z

What is the average distance between A and X, B and X, and C and X? What is the average distance between A and Y, B and Y and C and Y? What is the...

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