Some Maths Resources to Help You in Your ML Journey
I have been looking for content to improve my maths skills for ML. I have also noticed when scrolling a few threads many people did not find content that explains maths in an intuitive manner. Leading to a lack of belief in learning ML. But this does not have to be.
I’m with you, odd-looking characters and Greek letters don’t look welcoming. But they are some good teachers online that can demystify that experience.
Some of those materials are below:
3blue1brown Calculus and Linear Algebra series
I remember watching both of these series a while. And I will be watching them again. The narrator explores the topic without getting bogged down in the details. Feels like your discovering the maths with the original people who made calculus. In the linear algebra series, he does such a great job visualising vector space. You can see the various operations done to vectors and matrices in picture form.
3blue1brown Deep Learning series
Taking the concepts from the previous series and applying them to deep learning.
I’m sure you know about Sal Kahn by now. As you watched a couple of his videos. His video intuitively explains various topics. Also, show you the various hand by hand actions you need to take to do various calculations. Like matrix multiplication and calculating derivatives.
Mathematics for Machine Learning book
I tend to use this book as a reference guide if it’s a concept I want to check out. This book goes through the most important subjects relevant to machine learning and goes in-depth.
Mathematics for Machine Learning - Multivariate Calculus – Imperial College London
A multi-hour series explaining how calculus is used in deep learning. The material comes at the subject with a high-level view. But goes into sufficient enough detail to help you learn a lot.
Understand Calculus in 35 Minutes - The Organic Chemistry Tutor
A general overview of the subject. So you can be familiar with the concepts for deep learning later on.
NOTE: you won’t learn all of calculus in 30 minutes. But the video will help you get accustomed to the main ideas of the subject.
Now, these are resources that I have not used or have used very lightly but gotten good recommendations from various people.
So check them out:
This course talks about the linear algebra used in real computation. Not just Linear algebra done by hand.
Deep Learning book by Ian Goodfellow and Yoshua Bengio and Aaron Courville
From their website:
The Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular.
I have not thoroughly read all of the book. But I have used the notation page to understand maths symbols in various deep learning work.
An Introduction to Statistical Learning
A few people in this subreddit and the main subreddit have recommended this book. But I have never read it.
If you found this article interesting, then check out my mailing list. Where I write more stuff like this