Larry Chan's Blog: 2026

Friday, August 7, 2026

Math Self Study Progress Report 1 (Plus Initial Thoughts on AI as Math TA)

For those who are interested, you can read my previous post.

I began self studying real analysis around 2 weeks ago. I've chosen to follow Stephen Abbott's Understanding Analysis based on the comments I read on Amazon.

I have gone through the first chapter, including all the exercises. It covers sets, functions, real numbers, completeness, cardinality and Cantor's Theorem. This is my first time studying real analysis so I have no other books to compare to. But in my opinion, this is an excellent text for self study. I especially like the author's writing and organization. He spends quite some time talking about the motivations behind real analysis and why mathematicians invented it. The proofs were well written, and tend to be on the verbose side of things, but I think it's excellent for first time learners. The exercises reinforce what's being learned and shouldn't be skipped. They force me to think deeper about the material. I also find the epilogue very interesting as it gives readers extra information as well as some historical background regarding the materials covered in the first chapter.

It's only one chapter, but I already feel like it would be a long struggle. Calculus was my strong area when I was in secondary school but this is on another level. Well, technically real analysis NOT calculus! Nothing is intuitive. Definitions and theorems need be read 3-4 times in order to grasp what's being said. All the proofs are like magic. I honestly think that even if I time travelled back to my college days, I would not be spending less effort just because I was younger. So far, I find the Schroder-Bernstein Theorem most impressive and beautiful. I am surprised that its proof is left as an exercise to the reader. It deserves to be fully presented.

I've been using DeepSeek as a studying aid so far. I'm using the free version and I always turn on "expert mode." I don't know the difference between expert mode and think mode and what happens if I turn on both. DeepSeek says they're the same but judging from the output I'm not so sure. Here's how I've been using it.

Verify my answers to the exercises
Ask for hints and directions when I'm stuck in the exercises
Ask for simpler explanations, analogies, and concrete examples to build an intuitive understanding of theorems or definitions
Ask general questions as if it's a real TA (teaching assistant)

Here are my initial thoughts.

One word: INDISPENSABLE. I don't think I would have survived even the first chapter without AI's help.
In particular, as I mentioned above, Schroder-Bernstein Theorem was left as an exercise to the reader. Had DeepSeek not given me a concrete example, I would have never grasped the underlying idea, let alone proving it.
I was saved a number of times by AI when I was really stuck in the exercises. The hints either confirmed that I was thinking in the right direction and motivated me to continue, or gave me a kick in finishing a key step of a proof.
This is specific to DeepSeek. I cannot just take a photo/screenshot of an exercise and ask it to read the question for me. I had to type it in. It's tedious for things like subscripts and set operators. Especially with the latter, I had to describe them in words like "A is a subset of B intersect C."
On the other hand, I was quite surprised that DeepSeek was able to understand what I wrote ALL THE TIME, like a(n) was immediately recognized as "a sub n"; R, N, Q were immediately understood as set of real numbers, natural numbers and rational numbers respectively. At one point, I needed to explain my solution by describing a binary tree with only words. Nevertheless, it was able to grasp my idea correctly and draw that tree out for me. Sometimes I was afraid it might not understand what I said, like "infinite union of A(n)", but so far it never failed to understand me.
It had small misses but they're relatively easy to spot.
There were one or two big misses though, like when it thought a certain mapping is onto but actually not. In both cases, I just asked "Are you sure?" and it was able to recover from its mistakes.
It felt good when it praised the answers I gave or even when I asked questions 😆 For example, "This is a fantastic observation", "Your solution is more elegant than the standard approach." etc.
This is specific to DeepSeek: It couldn't graph functions in-place. It just gave me python code to run. On the other hand, both Gemini and ChatGPT can do that.
This is also DeepSeek specific. I don't understand the difference between Expert mode vs toggling the "Think" button. I asked DeepSeek to explain itself and it said they're the same. But by toggling on the "Think" button, I can see how DeepSeek is thinking. Expert mode does not show how it thinks. So, I'm not sure if they're really the same.

I am pleasantly surprised that AI can now do college level math. In my opinion, it's still more like a TA than a professor. But it's already quite impressive. It's good news for people like me who want to self study. I'm sure it's going to bring huge changes to college education in the near future.

What I'm planning to do is to use Gemini and DeepSeek alternatively for each chapter going forward. This way, I would be able to see how a frontier US model compares to a Chinese frontier model. I thought ChatGPT may be more relevant but I didn't have good experience with it. If I ask too many questions ChatGPT will throttle me. Gemini doesn't seem to have this problem and so I chose it instead.

My Math Self-Study Journey

I have always been interested in math since secondary school. In college, I studied electrical engineering but thought about switching to computer science because that would allow me to double major in math. I eventually abandoned that idea and completed my engineering degree. However, I always regret about my decision since I ended up working as a software engineer anyway.

I retired 5 years ago and the thought of self studying math came to my mind. Based on my own understanding, the bar of getting a math degree in an American college is actually not that high, compared to, say, my homeland Hong Kong. First of all, math majors in Hong Kong would have completed the equivalent of Calculus II in the US when they graduated from secondary school. Therefore, first year math majors in Hong Kong would start with real analysis right away. Contrast with the US, a majority of math majors here would spend the first 3-4 semesters studying calculus and linear algebra, and only began real analysis when they're junior, 2 years later than math majors in Hong Kong. To obtain a math degree at my college, University of Wisconsin-Madison, only 2 upper level math courses, Advanced Calculus I (real analysis) and Advanced Algebra I (abstract algebra), were hard requirements and the rest fell into the electives category. So, a math graduate in US may know nothing about complex analysis or topology, which would never happen in Hong Kong, at least not in my time.

But I digress.

Given my age and my lonnnnnnnnnnnnng hiatus from math, I wanted to set a relatively small achievable goal for myself. Therefore, I used the two hard requirements for math majors at my alma mater as a reference, and set out to self study real analysis as well as abstract algebra. Three years ago, I went through the abstract algebra YouTube series by Michael Penn. To be honest, I already forgot most of it. But it doesn't matter. It's for my own interest. I'm perfectly satisfied with the fact that I once studied it and understood it (isn't this true for most of what we studied in school anyway?). I also enjoyed the experience, and even the frequent struggles, when going through the materials because I find them beautiful.

Recently, inspired by Field Medalists Wang Hong and Deng Yu, I picked up where I left off 2 years ago, and began self studying real analysis. The difference this time is that AI has become much more powerful and I want to use it as a studying aid. In other words, AI would be my math TA (teaching assistant). I'll be reporting my progress, albeit somewhat irregularly. In particular, I would be sharing my experience with using AI as a tool for self studying college level math.

Stay tuned.

August 2026

P.S. I'm not trying to bash or look down on US math education. In my opinion, its math research is world class. I'm just trying to point out the difference between US and Hong Kong regarding math at the undergrad level. I genuinely believe that the hurdle is higher in Hong Kong than US in terms of graduate requirements for math majors. I'm not comparing the quality of the people or the universities.