Reading Period: July 14 - Present
1. Linear Algebra Done Right (P), by Sheldon Axler
Link: https://www.goodreads.com/book/show/309768.Linear_Algebra_Done_Right
This was a personal goal of mine to finish. I made it through the first 40 pages twice, at different points in different years of my life, but always got stuck. This time, I made an accountability bet to read the book start to finish within a two week time frame. It was not easy! I went through the book page by page with Claude, taking extensive notes and really trying to understand this field conceptually. Sheldon is a good writer, and the concepts and the way in which the material is taught is excellent. Leaving determinants to the end may or may not have been a good choice (this was my only exposure to the material), but the progression felt very natural and intuitive. At long last I understand what an eigenvalue is and how it relates to every other concept in linear algebra, and as a result am slowly chipping away at my inferiority complex with coworkers (who are all AI researchers who were PhDs in CS). I have a lot more work to do here, and to start I plan to review my notes extensively and continue to deepen my conceptual understanding. But overall this was quite a great introduction to a very dry topic.
2. The Structure of Scientific Revolutions (A), by Thomas Kuhn
Link: https://www.goodreads.com/book/show/61539.The_Structure_of_Scientific_Revolutions
Honestly, it was fine. I had pretty high expectations, but the core claims of the book are pretty simple. It's interesting to think of how science actually progresses (crisis, new paradigm, etc.), but the most interesting material simply doesn't take up many pages. The most important takeaway is that scientific process is way messier than commonly thought and often comes from unexpected (or disliked) areas. I try not to critique books for being too long, but I think Thomas could have spiced it up a little bit.
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