Joel Grus, a data scientist and software engineer, writes about Python, machine learning, and programming humor.
HN keeps linking his satirical post solving Fizz Buzz in Tensorflow, which commenters call a favorite and a classic.
I appreciate the categories, even if it wasn't personalized.
One idea for future development might be to add a point score based on Naive Bayes (which has been discussed previously). Not as a filter per say, but just as an indicator.
E.g. http://joelgrus.com/2012/02/16/hacking-hacker-news/
Sure, you can use a bookmarklet or write your own thing, but I've always thought it'd be an interesting idea to have a centralized like/hate server, where you could add different sources, say HN. That might be a different project, but adding a score based on the likelihood of "I'd read this" (not recommendation as I should note) would be a interesting in terms of skimming.
Let's say one of your users is in a hurry, he might want to skim the most (homogenous) likely news that'd interest him/her, whereas if it's late at night in leisure time, one might be more interested in new/abnormal stories.
Just a though. Good job.
There's a large number of such books, though none that are as authoritative with respect to Python (this is a statement about the size of Python's community vs. R, not necessarily about the authors):
- via Wes McKinney, creator of pandas (which makes Python about as close to R as you can get): https://www.amazon.com/Python-Data-Analysis-Wrangling-IPython/dp/1449319793
- http://joelgrus.com/2015/04/26/data-science-from-scratch-first-principles-with-python/
There are a bunch of books specific to machine learning too though I haven't read them myself.
The example of the OP also reminded me of Max Howell, but the only other similar story was the one of the guy who was prompted to implement fizzbuzz, and attempted to do so in Tensorflow[0]. In the latter case, I can totally understand that he might've been turned down with such a cocky (though funny) attitude, if the anecdote is true.
Are there other notable cases of people who got turned down in spite of - or because of - their extensive knowledge in a case like this?
[0] https://joelgrus.com/2016/05/23/fizz-buzz-in-tensorflow/
This is a delightful read, which reminds me of two other articles. The first is also a caricature of the technical interview, solving FizzBuz with Tensorflow: https://joelgrus.com/2016/05/23/fizz-buzz-in-tensorflow/
The second is a explanatory story, or "discovery fiction" as the article classifies itself: https://paulbutler.org/2022/what-does-it-mean-to-listen-on-a-port/
I love these humorous yet pedagogic technical writings, woven with a bit of literary eloquence and down-to-earth narrative. Thank you for this.
I think a good book that is closer to Andrew NG's course would be the An Introduction to Statistical Learning (ISL)[1]
Depending on your learning style, Data Science from Scratch[2] might be another good option.
BTW, neither of these uses Octave like Andrew NG's course does. The first one uses R and the second uses Python.
[1]: http://www-bcf.usc.edu/~gareth/ISL/
[2]: http://joelgrus.com/2015/04/26/data-science-from-scratch-first-principles-with-python/
Interestingly, I really liked [spoiler] too, but I also hoped that this blog post could be reused in future language fights (and it is a shame that [spoiler] could prevent that). Potentially a revised version of this blog post could be helpful.
Anyway, this link (http://joelgrus.com/2013/12/24/why-programming-language-x-is-unambiguously-better-than-programming-language-y/) is decent enough for copying and pasting.