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# Linear algebra, Neural Network Mathematics, and other nerd stuff - Jan/Feb 2021

### new year of things and stuff

Hi, reader, welcome back to my bimonthly organized rant about the activities I did over the past bi-months. Thanks for checking this out.

I was able to do a few things this half-season, and that’s what I’m going to outline, although I’ll assume you know that since that’s pretty much common sense, I don’t know why I wrote this, I don’t know why I’m still writing this paragraph.

If you don’t know who this Adam Dhalla person is, I like learning about the mathematics behind machine learning algorithms, programming them, and seeing how they can help model & understand stuff in the natural world. You can visit my website adamdhalla.com for more stuff.

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### Linear Algebra

For two months starting on the 25th of December 2020, I taught myself linear algebra by using MIT OpenCourseware’s online course 18.06 taught by Gilbert Strang and by following along with reading and doing exercises in his textbook (*Linear Algebra and It’s Applications, 4th edition*).

It was considerably hard but also quite fascinating, and really showed the ‘beauty’ of math - the ability to easily transition from two to 4213 dimensions is amazing.

I wrote a bunch of articles on my medium on the linear algebra I was learning about. The article, imo, with the most important subject matter, is the one on the *Normal Equation, *which is a matrix equation used to find a linear line of best fit. I explain it in terms of vector projections and subspaces and you can read it here.

I’m currently ordering his [Gilbert Strang’s] sexy new textbook, *Linear Algebra and Learning From Data, *and once I get that (sometime mid march since I don’t have amazon prime) I’ll follow along with his new course, *Matrix Methods in Data Analysis, Signal Processing, and Machine Learning, *or 18.065, for more applied things.

### Neural Network Mathematics

Running theme might be how math dominates here, but never mind that. I devoted a couple weeks to understanding how multivariable calculus operates in the context of neural networks and backpropagation, and one thing lead to another and I ended up recording and editing a comprehensive 5hr lecture series on the complete mathematics for neural networks.

I’m no Andrew Ng but it covers pretty much everything you need to know the mathematics behind a standard ANN. Here’s the syllabus if you’re curious.

Alright enough of that. What else you got?

### Non-learning Artificial Intelligence

I was lucky enough to get my hands on the 2020 copy of *Artificial Intelligence: A Modern Approach, *a beautiful (and large) textbook by Stuart Russell and Peter Norvig.

Working my way through the textbook, I wrote articles and coded a few systems. I’ve currently stopped going through it for now while I attempt a few other problems.

I wrote articles and coded rule-based systems like above as well as genetic algorithms.

### Writing on Modern Transcendentalism

I usually don’t do a whole feature thing about an article, but my article on the philosophy of transcendentalism got quite a bit of notice.

I was fortunate enough for my article to be translated into Greek and featured in the Greek philosophy publication, *Orthos Logos. *You can take a look at the translation here (press the link below the blue button if you want to see the whole thing).

### Small things

I did a few other small things that aren’t worth big headlines, so here are those things

I taught myself

*Julia*which is an awesome language and everyone should use it for all things. It’s as simple semantically as python but runs as fast as C.I wrote eight or so articles.

I made my personal website.

A mobile game on birds and conservation I co-created, secured funding and is set for launch in spring of 2021 (this is actually not a small thing but I can’t say all the details yet).

My practical application task for the next couple months - programming a segmentation and computer vision software to detect protein localization within images of many cells using weakly labeled data. more details later.

Thanks for reading this. See you in two months.