I find her book to be more practical and humble, though perhaps less feature-complete than the book from this post.
IMO Kashani and Ivry’s writing feels a little uppity and needlessly offputting — for example, logistic regression is lumped under Kindergarten in the table of contents. Sure it’s fundamental, but implying it’s not useful anymore in our age of deep learning (listed under “Bachelor’s,” of course) is a little myopic/insulting, no? Students who feel pandered to by know-it-alls probably learn worse than students whose skills are being collaboratively built up by enthusiastic mentors.
This feeling was particularly loud from the foreward, ToC, and intro; maybe it gets better after…?
> Students who feel pandered to by know-it-alls probably learn worse than students whose skills are being collaboratively built up by enthusiastic mentors
That helps put words to a feeling I've had for quite a while now about certain types of educators. I couldn't put words to it but this makes sense in retrospect. Its almost like they're saying "look what I can do! I'm a mathemagician!" vs putting the students learning first.
If I had a dollar for every top-candidate-on-paper who missed basic questions related to interpreting regression model coefficients correctly, I’d have a lot of extra dollars!
It's free but the Amazon reviews are ... not favorable. I'd love to know of books or resources like this that are good collections of real AI/ML/DL interview questions though!
It was a research scientist role. The company I will omit because frankly I heard multiple companies doing it (to the point that at least two sources recommended to me to read minGPT before interviewing, so it is "invert binary tree" but for RS interviews).
Honestly it will be a miracle if an indie game dev like me gets the job of a machine learning research scientist. Can't say they asked the wrong questions.
Sorry it was a bit half joking. I am an indie game dev but at the same time I also had prior experience doing ML engineering.
FWIW some types of game devs are actually versatile people for ML engineering. They can write low level code; they can manipulate bitmaps; they have keen-enough eyes to tell you something is wrong with your image dataset. I don't think game devs are a bad choice.
I find her book to be more practical and humble, though perhaps less feature-complete than the book from this post.
IMO Kashani and Ivry’s writing feels a little uppity and needlessly offputting — for example, logistic regression is lumped under Kindergarten in the table of contents. Sure it’s fundamental, but implying it’s not useful anymore in our age of deep learning (listed under “Bachelor’s,” of course) is a little myopic/insulting, no? Students who feel pandered to by know-it-alls probably learn worse than students whose skills are being collaboratively built up by enthusiastic mentors.
This feeling was particularly loud from the foreward, ToC, and intro; maybe it gets better after…?