{"id":1027,"date":"2018-12-12T02:48:53","date_gmt":"2018-12-12T02:48:53","guid":{"rendered":"http:\/\/kusuaks7\/?p=632"},"modified":"2023-07-03T11:31:26","modified_gmt":"2023-07-03T11:31:26","slug":"learning-ai-if-you-suck-at-math-part6-math-notation-made-easy","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/ai-ml\/learning-ai-if-you-suck-at-math-part6-math-notation-made-easy\/","title":{"rendered":"Learning AI If You Suck at Math – \u200aPart 6\u200a – Math Notation Made Easy!"},"content":{"rendered":"

Ready to learn Machine Learning? Browse<\/em><\/strong> Machine Learning Training and Certification courses<\/a> developed by industry thought leaders and Experfy in Harvard Innovation Lab.<\/em><\/strong><\/p>\n

Welcome to part five of Learning AI if You Suck at Math. If you missed\u00a0part 1<\/a>,\u00a0part 2<\/a>,\u00a0part3<\/a>, part4<\/a>, and part5<\/a>\u00a0be sure to check them out.<\/p>\n

If you\u2019ve followed parts\u00a0of this series you know that you really don\u2019t need a lot of math to get started with AI. You can dive right in with practical tutorials and books on the subject.<\/p>\n

However, there are lots of reasons to learn mathematical notation.<\/strong><\/p>\n

Maybe you just want to stretch yourself and learn a new skill? Learning something outside of your comfort zone is a fantastic way to keep your mind sharp.<\/p>\n

Or perhaps you\u2019d like to start reading a few of the papers on\u00a0arXiv<\/a>? You might even want to implement an exciting new research paper idea instead of waiting for someone else to put it on Github.<\/p>\n

To do that you\u2019ll need to know how to read those funny little symbols.<\/p>\n

Maybe the greatest reasons to learn math notation is that it let\u2019s you express complex ideas in a very compact way.<\/strong><\/p>\n

Without it, it would take pages and pages to explain every equation.<\/p>\n

Yet even with all the resources out there it can still be intimidating to face a string of those alien characters.<\/p>\n

Have no fear. I\u2019m here to help.<\/p>\n

I\u2019ll show you that learning these symbols is not as hard as you think. But there are a few things holding you back.<\/p>\n

First, if you\u2019re like me, you hated math as a kid. I\u2019ve discovered the key reason is that my teachers never bothered to answer the most important question:<\/p>\n

Why?<\/em><\/p>\n

Why am I doing this? How does this\u00a0apply<\/em>\u00a0to my life?<\/p>\n

They just slapped a bunch of equations on the board and told me to memorize them. That didn\u2019t work for me and I\u2019m betting it doesn\u2019t work for you.<\/p>\n

The good news is that if you\u2019re interested in the exciting field of AI it\u2019s a great answer to that question!<\/p>\n

Now you have a\u00a0reason to learn<\/em>\u00a0and\u00a0apply it to real world problems<\/em>. The \u201cwhy\u201d is because you want to write a better image recognition program or an interface that understands natural language! Maybe you even want to write your own algorithms some day?<\/p>\n

The second thing holding you back is the plethora of horrible explanations out there. The fact is most people are not very good at explaining things. Most of the time people define math terms with more math terms. This creates a kind of infinite loop of misunderstanding. It\u2019s like defining the word \u201celephant\u201d by saying \u201can elephant is like an elephant.\u201d Great. Now I understand. Not!<\/p>\n

I\u2019ll help you get to firmer ground by relating it to the real world and using analogies to things that you already know.<\/p>\n

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I won\u2019t be able to cover all the symbols you need in one article, so you\u2019ll want to pick up this super compact guide to math symbols,\u00a0Mathematical Notation: A Guide for Engineers and Scientists<\/strong><\/a>\u00a0<\/strong>by Edward R. Scheinerman, if you haven\u2019t already. (It was a late addition to my first\u00a0Learning AI if you Suck at Math<\/a>\u00a0article, but it\u2019s become one of my most frequently used books. It\u2019s filled with highlights and dog eared pages. As my knowledge of various math disciplines expands I find myself going back to that book again and again.<\/p>\n

Let\u2019s get rolling.<\/p>\n

To start with,\u00a0what is an algorithm<\/strong>?<\/p>\n

It\u2019s really nothing more than a series of steps to solve a particular problem. You use algorithms all the time whether you know it or not.<\/p>\n

If you need to pack lunch for the kids, drop them off at school and get your dry cleaning before heading to work you\u2019ve outlined a series of steps unconsciously to get from the kitchen to the office. That\u2019s an algorithm. If your boss gives you six assignments that are competing for your time, you have to figure out the best way to finish them before the end of the day by choosing which ones to do first, second, in parallel, etc. That\u2019s an algorithm.<\/p>\n

Why is that important?\u00a0Because an equation is just a series of steps to solve a problem too.<\/strong><\/p>\n

Let\u2019s start with some easy symbols and build up to some equations.<\/p>\n

Math is about transforming things<\/strong>. We have an\u00a0input<\/strong>\u00a0and an\u00a0output<\/strong>. We plug some things into the variables in our equation, iterate through the steps and get an output. Computers are the same way. Now, most of the magic behind neural nets comes from three branches of math:<\/p>\n