Oct 16 2024
- Former business school classmate, but also developer, took this class and recommended it
- He sent along some primers on Linear Algebra and Probability
- I wrongly read the Probability one first, and then after reading the Linear Algebra primer it was obvious after page 2 that I read them out of order
- Linear Algebra - I’ve been helping my high school son with his math for years (I’ve had a lot of math as an engineer at Lehigh) but I had this thread going in the back of my head saying, “Do I need to learn this, and if so, then how deeply?”
- I needed a break and was looking at some other resources for learning AI/ML, that led me to this set of YouTube videos, the first one here: Linear Algebra - Math for Machine Learning
- Wow, this video really started to connect the dots on what I was reading in these primers and helped me as a developer to understand the analogous meanings of models, inputs/outputs, etc.
- Many objects are represented by arrays in machine learning:
- Almost always: data (inputs and outputs)
- Very often: models (equivalent to programs)
- Often: internal computations of models (local variables)
- “Do I need to learn this?” → yes
- I’m more motivated now to read this primer
- “If so, then how deeply?” → TBD…I am pretty sure this is going to be a circuitous journey on math depth, not a linear one.