What's everyone's favorite package/approach to engage with tensorflow/keras in R? (trying to teach a short course, and don't want to require ppl to know python; unfortunately reticulate->miniconda seems to have a bug in windows =[)
Just came across a nugget of wisdom from a mathematician that I thought I would share: "A drunk man will find his way home, but a drunk bird may get lost forever" - Shizuo Kakutani
Wanted to share a lovely paper primarily by @StatTZhang that was just accepted at Annals of Statistics on efficient non-parametric regression using stochastic optimization! arxiv.org/abs/2104.00846 Less technical summary to follow... (1/n)
Some neat points: Polynomial terms do not need to be orthogonal wrt our data! In fact this works super generally for regression functions in RKHS/Sobolev Ellipsoids. Even if we don't care about the online setting, this is a very computationally efficient approach. (7/n)
What does this say about deep reinforcement learning? Perhaps we can consider starting with a simple network and scheduling additions to the topology as we go --- though possible that we want to decrease step size for those additions. (8/n) Sorry this thread was so long!
Why aren't there any good golden snitch costumes for infants? Also, how dare you suggest I plan to dress my baby up as the golden snitch while I watch the secrets of dumbledore... (when it is streamable)
Using familiar terms, you conflate causality and correlation in this offhand tweet. CRT does the hard work of identifying causal structures that lead to systemic inequities… you compare it with a non-rigorous opinion piece that treat root causes as personal opinions.
We must remember that the holocaust was preceded by a successful program of "critical race theory". In April, 1938, Nobel Prize winner J. Stark explained in the British weekly "Nature" why Jewish professors at German universities must be replaced by "under-represented" Germans.
This is quite useful (especially for people looking to get into ML); my summary of the thread is that the ability to engage in simple, fit-for-purpose modeling, and then validate communicate your models trumps all else.
Universities do a terrible job teaching machine learning.
Not only do they give you critically out-of-date information, but they focus most of their time on the least important aspects.
Here 5 things everyone in industry WISHES your professor taught you:
I think something intimated, but unwritten, is the value in understanding the FEW situations where a very simple modeling technique will NOT suffice. Most of the time, at the modeling stage, glm(...) or glmnet(...), or xgboost(...) is totally fine.
Our paper came out on Pi day! We describe how to train and tune neural networks for high-dimensional data using only TWO hyper-parameters (doi.org/10.1002/sam.11579). We think it's easier, so we've called it EASIER-net. @StatsSimon