n3ur0n

6 ms·

1.
by n3ur0n 3y ago
I would say UDL should be very accessible to any undergrad from a strong program. I would not call the notation ‘dense’ rather it’s ‘abused’ notation. Once you have seen the abused notation enough times, it makes just makes sense. Aka “math
2.
by n3ur0n 6y ago
Given your background, I think it would be worthwhile for you to pick up ESL [0] and read some relevant sections (supervised/sparse/linear methods). It's a great book and a good starting point for thinking about ML methods fo
3.
by n3ur0n 6y ago
Depends on the scope of the project. Would the goal be to come up with a better algorithm for cell classification based on histological images? Or to apply an existing algorithm to a new dataset? The former would be quite difficult without
4.
by n3ur0n 6y ago
I think it's partly the incentive structure that is to be blamed. Historically, quantitative PhDs in healthcare(medical physicists, statisticians, comp. genetics) have been underpaid (in my opinion). Now with FAANG and Quant Funds will
5.
by n3ur0n 6y ago
I do respect your experience and take on the matter, however, let's replace this statement: "I'm an eye surgeon and self-taught machine learning practitioner, I started to learn Python in 2016 when the deep learning hype was
6.
by n3ur0n 6y ago
As a rising 5th year PhD in ML -- I could not agree with this advice more! I have very hands-off advisors. I spent the first 3 years "wandering the woods to find something". Last year, I really had to sit down and think about how
7.
by n3ur0n 7y ago
> just means trying out this statement trivializes a very hard problem. > literally just means trying out, or simulating Simulating is an incredibly hard problem and MC methods and theory is an incredibly rich area of study. Some tool
8.
by n3ur0n 8y ago
There is really no difference. You can frame the Kalman filter as a Bayesian posterior inference problem. For example, for a stationary linear Gaussian model, you have a transition model of the form: z_t = A z_{t-1} + B u_t + e where e ~ Ga