Isn't gbm just trees? Been using it in spark for close to an year and never bothered to learn the difference. I just run both and choose the best algorithm.
I have been working on ML for close to two years and I can't answer most of these questions. Guess I am not cut out for Google
This seems like a set of questions for Analysts, rather then Engineers.
If you apply for a ML SWE role, these are unlikely to be the questions you get, though with a company as large as Google, who knows what your interviewer will bust out.
GBM is trees, and I think the question is to explain why you would want trees, but I think the trick for fitting arbitrary differentiable loss functions in a GBM is pretty nifty.
This is just a sample of the questions they ask. I think Google interviews are dense and go back to first principles which is what I have learned as well.
AI courses on Udacity are great for self starters. If that peaks your interest it should help you accelerate your learning further as well. I have learned the same way. Hours and effort would differ from person to person.
Usually we are talking about having a PhD in an AI related field. Again I do not have stats to corroborate this information but going through their research blogs the culture is heavily research oriented.
What is the pay like? The reason I ask is because I am super interested in the field but sacrificing 1.25 million dollars worth of pay (5 years of lost google senior software engineer pay to get masters and PHD and that is not even counting the time value of money) in a risky career move should benefit with large compensation IMHO.
Isn't gbm just trees? Been using it in spark for close to an year and never bothered to learn the difference. I just run both and choose the best algorithm.
I have been working on ML for close to two years and I can't answer most of these questions. Guess I am not cut out for Google