That book probably doesn’t go much further than neural networks with 1 hidden layer. Maybe 2 hidden layers at most.
IMO, statistics is about explaining data. Regression is useful to explain how parameters relate to each others. Statistics that don’t help us understand data isn’t useful statistics.
Modern machine learning has strayed far away from data explanation. Now it’s common to deal with more than a dozen hidden layers. It might have roots in statistics, but mostly it’s about brute forcing any curve to the data. It doesn’t help us understanding the data better, but at least we have approximated some function.
If you have any ideas about how statistics are at the bottom of LLMs, you are probably thinking about some other ML technique.
It might have roots in statistics
Care to reiterate?
Just because wheels have roots in horse wagons doesn’t mean cars are horse wagons
Wheels have roots in carriages
Whether you are a badass with a horse carriage or a cuck with a horseless carriage doesn’t really drive home your point
