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Machine Learning Quick Reference

You're reading from   Machine Learning Quick Reference Quick and essential machine learning hacks for training smart data models

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Product type Paperback
Published in Jan 2019
Publisher Packt
ISBN-13 9781788830577
Length 294 pages
Edition 1st Edition
Languages
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Author (1):
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Rahul Kumar Rahul Kumar
Author Profile Icon Rahul Kumar
Rahul Kumar
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Table of Contents (13) Chapters Close

Preface 1. Quantifying Learning Algorithms FREE CHAPTER 2. Evaluating Kernel Learning 3. Performance in Ensemble Learning 4. Training Neural Networks 5. Time Series Analysis 6. Natural Language Processing 7. Temporal and Sequential Pattern Discovery 8. Probabilistic Graphical Models 9. Selected Topics in Deep Learning 10. Causal Inference 11. Advanced Methods 12. Other Books You May Enjoy

Introduction to vectors


Before moving on to the core topic, we would like to build a foundation for getting there. Hence, this segment of the chapter is very important. It might look familiar to you and many of you will be cognizant about this. However, going through this channel will set the flow.

A vector is an object that has both a direction and magnitude. It is represented by an arrow and with a coordinate (x, y) in space, as shown in the following plot:

As shown in the preceding diagram, the vector OA has the coordinates (4,3): 

Vector OA= (4,3)

However, it is not sufficient to define a vector just by coordinates—we also need a direction. That means the direction from the x axis.

Magnitude of the vector

The magnitude of the vector is also called the norm. It is represented by ||OA||:

To find out magnitude of this vector, we can follow the Pythagorean theorem:

OA2 = OB2 + AB2

= 42 + 32 

= 16 + 9

= 25

Hence:

OA = √25 = 5

||OA||= 5

So, if there is a vector x = (x1,x2,....,xn):

||x||= x12 + x22+........

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