Linear Algebra Essentials with Numpy (part 1)
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Linear Algebra Essentials with Numpy (part 1)
Sep 21 ·9min read
Ah, math. You can’t avoid it forever. You can try, and then try harder, but sooner or later some basic intuition behind it will be needed, provided that you are serious in your endeavors to advance your career in data science.
When it comes to linear algebra, I really like this quote:
If Data Science was Batman, Linear Algebra would be Robin.[1]
It neatly captures the essence in a non-technical way.
Why should I study linear algebra?
Great question.
Simply put, if you understand it, you will be able to develop a better intuition for machine learning and deep learning algorithms and you won’t treat them as black boxes. Furthermore, you would also be capable of developing algorithms from scratch and making your own variations of them.
Not only that, but you will also be considered as a cool kid, like these two:
So awesome.
Why should I read this post?
This post certainly won't teach you every nitty-gritty detail about the topic, there are a plethora of books which cover those. The post also won’t dive into proofs, again, read a book from some math or tech faculty if you are into that. This post will, however, provide you with an essential intuition behind linear algebra (and some light calculations) in 14 different areas.
Yes, you’ve read that right. I’m planning to cover 14 different topics in two articles. It will take you some time to read it (and for me to write it), but if you are new to this I would strongly advise reading 2–3 topics per day at most, and then searching the web for more practice problems.
How is the article structured?
So each of the topics is divided into 3 parts:
- Theoretical explanation
- Example (by hand calculations)
- Implementation in Python (with Numpy)
I won’t lie to you — it will be a lot of work. This first part will cover Vectors , and here’s the topic list:
- Addition
- Scalar Multiplication
- Dot Product
- Norm
- Unit Vector
- Angle Between Vectors
Now when the introduction is over, let’s not postpone the inevitable. Grab a cup of coffee (or something stronger) and come with me to the amazing world of linear algebra.
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