3.1 Rules vs. Learning
To understand how machine learning works, it's a good idea to start with the traditional and maybe alternative approach: rules.
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To understand how machine learning works, it's a good idea to start with the traditional and maybe alternative approach: rules.
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In this exercise, we are going to use the popular MNIST data set of handwritten digits to work out the problems when we try to define rules for a seemingly simple task: recognize and classify handwritten digits.
Search online for version of the MNIST data set in CSV format. If you can't find one, you can refer to . Download the data set, extract the file, and open it in a text editor.
What does an image look like to a computer?
What do all the numbers in one line mean?
To get a better understanding of how a computer sees and displays images, add a feature to your web app that takes a representation of a digit as an array of numbers and display it on the website pixel by pixel. Take a look at the and especially the following functions:
createCanvas()
square()
stroke()
fill()
Additionally, if you want to add the rows and column labels:
text()
textSize()
You can use the following array of the number 3 shown below as your input data:
Now that we have a better understanding of how a computer represents images, let's try to come up with rules that allow us to recognize the particular digit behind a long list of numbers. For simplicity, start with only the number 3 and write a program that outputs true or false. true meaning the list of numbers is a 3, and false it is not. This is called binary classification because we only distinguish between two answers.
How can you teach this seemingly easy task (for humans) to a computer?
Why is it so difficult to articulate the rules that determine whether it's a 3 or not?
