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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

Linear algebra 101

I want to take a detour to talk about linear algebra. It's featured quite a bit so far in this book, although it was scarcely mentioned by name. In fact linear algebra underlies every chapter we've done so far.

Imagine you have two equations:

Let's say and is and , respectively. We can now write the following equations as such:

And we can solve it using basic algebra (please do work it out on your own): and .

What if you have three, four, or five simultaneous equations? It starts to get cumbersome to calculate these values. Instead, we invented a new notation: the matrix notation, which will allow us to solve simultaneous equations faster.

It had been used for about 100 years without a name (it was first termed "matrix" by James Sylvester) and formal rules were being used until Arthur Cayley formalized the rules in 1858. Nonetheless...

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