Maybe I am wrong, but I have always perceived that the influence of Japanese culture on America is no longer as strong as it was in the 90s and early 00s. That could be my childhood bias though.
pretty much the entire deep learning community is still using python 2.7. I have moved onto python 3.6, but Julia is still an unsafe bet because it has an unreliable community behind it.
I actually do the same thing. Do the dependency install inside of a docker container in order to build the .zip. This is absolutely essential for compiled libraries in python or anything that involves postgresql. If you attempt to install psycopg on mac and then deploy that on lambda it will fail. So you either need a prebuilt version for ubuntu that you keep around, or you need to build in a docker container. We just build inside a docker container.
There are probably many on HN who can answer this better than me, and it of course depends on what role you are interviewing for . But IIRC, for Engineers the process involves a technical phone screen, followed by a second technical interview plus a behavioral/EQ interview. After that, your application goes before a hiring committee.
I can provide 100% assurance of that. We choose projects based on "Doing the most good, for the most people, in the most need". It could be easily argued that the vast majority of our work, focuses on helping vulnerable populations. I've been here 10 Months and never had a single ethical concern. Furthermore, I firmly believe this is the kind of organization that would be fully supportive of anyone vocalizing any ethical concerns they personally had, in fact this would be encouraged.
I have not yet committed to a deep learning framework, as up until now, I was mostly either using scikit-learn or building neural networks from scratch, straight numpy (lol)
I've heard a nice thing about Keras is that it forms more of an abstraction on top of other libraries, though I could be misunderstanding.
I want to know what this means for observational astronomy. Can we put this in the eyepiece of a telescope and discern features in nebulae that otherwise look like gray blobs to unaided vision
They are actually very public about the fact they are not a machine learning company and are more or less opposed to machine learning. Their goal has always been to wrangle data and make it useful to human decision makers. not automate decision making.
I was once diagnosed with ADHD myself as a child. I have been unmedicated for 8 years now and been quite successful career wise, and academically. Though I do wonder if I could do better had I not had ADHD.
its not fair to conduct these studies solely on educational outcomes because educational outcomes in the west largely depend on how well you conform to the mean societal expectation of a successful archetype. many people have unique educational styles (ADHDers included). I myself personally learned more from the internet and reading books than I ever did in school.
I suspect successful engineers, start up CEOs, hacker types have higher prevalences of "learning disorders", ADHD, etc. Many did poorly in school, though many also excelled. I also suspect the same demographics are true of musicians and other artists.
The MAJOR caveat here, is that while these groups probably have higher prevalences, and these fields are more or less supportive of people with these issues, very few ADHDers can break into these fields and the average ADHDer probably will perform worse in life than the average person without.
Mostly conjecture, based on my own anecdotal evidence, but worth consideration.
This is absolutely true, the one caveat is that you can explain the significance of features and the relationship to the response variables in simpler terms.
another point is that Linear Regression IS Machine/statistical Learning. Sure its been around for more than 100 years before computation, but regression algorithms are learning algorithms.
Arguing for more linear regression to solve a firms problems, is equivalent to arguing for machine learning. Now, if instead he wanted to argue that the vast majority of a businesses prediction problems can be solved by simple algorithms, that is most likely true. but economic impact of this is still a part of the economic impact of machine learning.