TK Changelog #26
Hi, it's TK!
Welcome back to another changelog newsletter, this one describing all the work, studies, and actions I did in July 2024.
So let's go!
Machine Learning
The first and most important project has been my Machine Learning studies so let's start with it.
This month I've made a lot of progress in the mathematics foundation for ML. I've finished the Essential Math for Machine Learning course, where I've learned a lot and refreshed my math knowledge.
The course was really interesting because it not only taught the math concepts but every new concept had a lab class where we used Python to express math calculations and concepts.
It started refreshing my memory of algebra basics, going to calculus, linear algebra, and statistics.
In calculus, the most fundamental concepts I've learned were derivatives and differentiation.
In linear algebra, they were vectors, matrices, and systems of equations.
In statistics, I got insights into the type of data, different types of visualization, Measures of Central Tendency, important concepts like variance, standard deviation, how to compare data, and probability theory.
You can access all my notes and code in this Github repository.
Another part of this study is the Statistical Learning book I've been reading. I feel I could do a better job because last month I made a lot of progress in the introduction and linear regression, but this month I only read the classification chapter, which was really good.
In that chapter, I learned about different ways to approach a classification problem. Going from linear regression to logistic regression to multiple logistic regression to LDA and QDA. The concepts are simple but it took me a while to digest the math (which I didn't get entirely tbh — I need to recap this later on).
You can follow the progress of my studies and all my notes in this repo.
Another part that I'm very excited about is that I started my Deep Learning studies. You can follow my progress, which has been slow, in this repo and this thread.
In terms of ML studies, this was all I did this month. I feel I'm making progress towards my goal and starting to have a better understanding of ML.
Content Creation & Blog
This month I put a lot of time into writing and improving my website. As Steve Jobs would say:
I think this description resonates deeply with how I've been trying to express myself through iamtk.co, where I share every progress of my life and learnings.
In terms of content, I wrote 6 new posts:
There are 3 venues I'm writing about, each as a section in the newsletter:
Machine Learning & AI: my studies, research, and everything I've been reading and learning
Software Engineering: engineering in general, but more specifically about performance engineering now
Meta-learning: everything about studying, learning, productivity, and mastering my craft
The more I write about ML and data science, I saw the need for improvements in the blog. This month I implemented KaTeX to render math symbols and equations as TeX. See how it's rendering in the following video example:
It's just beautiful! ✨
Another website improvement is about rendering tables like github renders them. It's especially nice to have this implementation for data science where I want to show some statistical data.
Books
I have new books that arrived this month. My goal is to focus on ML, Deep Learning, bioinformatics, and application in life science.
This month I finished two books: The Art of Learning and Problem Solving 101.
Meta-Learning
I started using Anki/Spaced repetition for mathematics (calculus, statistics concepts) and machine learning
Because of that, I searched for effective approaches to generate concepts I've been learning. I found an interesting way to generate the cards for me: ChatGPT: Let it write Anki cards.
Interesting Links
Some links I found interesting to read and consume this month: