"If you give me six lines written by the hand of the most honest of men, I will find something in them which will hang him." - Cardinal Richelieu (disputed)
If you're genuinely eager to learn probabilistic programming then by far the best resource I've found is a book called Statistical Rethinking by Richard McElreath. His (almost finished) draft for the second edition is up here:
http://xcelab.net/rmpubs/sr2/statisticalrethinking2_08dec19....
Dr. McElreath also posts his lectures on youtube. The R code in the book and his lectures use a library/package he wrote which provides a wrapper to simplify building Stan models. The code has also been translated to PyMC3 on Python.
It might look like a huge amount of content but this course leads you very gently through key concepts, keeping the mathematics to a minimum. Don't be put off if you don't know the R language. The concepts are more important than the programming language and the code examples are kept simple.
If you make it through Statistical Rethinking then you might consider picking up Doing Bayesian Data Analysis by John Kruschke (a.k.a. the puppies book). I've found DBDA to be heavier going than SR but Kruschke takes a different approach to McElreath which can be useful if you get stuck on a concept, need more detail or just want a different angle on the subject.
While Kalman and Bayesian Filters in Python is a superb resource, probably the best out there, my recommendation for anyone new to the field would be to do Sebastian Thrun's free Artificial Intelligence for Robotics course [1] as an intro, then go through Labbe's work afterwards.
Thrun's course is more accessible and even more hands-on than Labbe's content. As a bonus he also covers Particle Filters,PID control, Search and SLAM (which cam out of Thrun's PhD thesis).
It shouldn't take three plus years to fix an upgrade. The goal was good, WebExtensions are a significant step forward as a concept. Planning, execution and post-transition management sucked.
If Firefox had treated TabMixPlus (TMP) and other extensions as first-class citizens when they introduced quantum, I can guarantee I'd be using Firefox today (along with all the less technical people in my extended network, who I'd install it for).
Unfortunately, during the transition to quantum and WebExtensions the developers blocked add-ons making changes to the Firefox interface, which crippled TMP and a variety of other add-ons. To suddenly have your favorite add-on crippled is a little painful but what made me walk away was the tone of responses from Mozilla people on the boards and the bug reports. It ranged from dismissive, to arrogant, to angry which, particularly given how quickly the transition took place, just added insult to injury.
It seemed like Mozilla did get the message by the end of 2017 that their approach and response to add-ons had alienated many users. One of their 2018 visions included a statement that 'In 2018, extensions will be one of the reasons why people choose and use Firefox.' Unfortunately, when I looked at the TMP message boards last year I still saw very little in the way of signs of cooperation and encouragement from Mozilla. The TMP developer, onemen, still seems to be trying his best to produce a suite of extensions to reproduce the lost functionality and to be fair to Mozilla they have been moving obstacles out of the way but the pace is glacial.
Chrome may be creepy and invasive but right now it's far more flexible and remains a smoother experience. I'd really love to switch away from Chrome but I won't trade it for an inflexible Firefox UI. If Mozilla could loosen up on the UI restrictions, demonstrate that they're doing everything possible to make the product friendly for add-on developers, and somehow get themselves around to replicating, or helping to replicate, TMP and other crippled add-ons then I would enthusiastically consider switching.
If Firefox had treated TabMixPlus (TMP) and other extensions as first-class citizens when they introduced quantum, I can guarantee I'd be using Firefox today (along with all the less technical people in my extended network, who I'd install it for).
Unfortunately, during the transition to quantum and WebExtensions the developers blocked add-ons making changes to the Firefox interface, which crippled TMP and a variety of other add-ons. To suddenly have your favorite add-on crippled is a little painful but what made me walk away was the tone of responses from Mozilla people on the boards and the bug reports. It ranged from dismissive, to arrogant, to angry which, particularly given how quickly the transition took place, just added insult to injury.
It seemed like Mozilla did get the message by the end of 2017 that their approach and response to add-ons had alienated many users. One of their 2018 visions included a statement that 'In 2018, extensions will be one of the reasons why people choose and use Firefox.' Unfortunately, when I looked at the TMP message boards last year I still saw very little in the way of signs of cooperation and encouragement from Mozilla. The TMP developer, onemen, still seems to be trying his best to produce a suite of extensions to reproduce the lost functionality and to be fair to Mozilla they have been moving obstacles out of the way but the pace is glacial.
Chrome may be creepy and invasive but right now it's far more flexible and remains a smoother experience. I'd really love to switch away from Chrome but I won't trade it for an inflexible Firefox UI. If Mozilla could loosen up on the UI restrictions, demonstrate that they're doing everything possible to make the product friendly for add-on developers, and somehow get themselves around to replicating, or helping to replicate, TMP and other crippled add-ons then I would enthusiastically consider switching.
I hit a roadblock with trying to read Stata files into Pandas a few months ago. I discovered that not all versions of Stata file formats are supported by Pandas in Python. R has much better support for Stata files.
With the help of Feather, which was written over Arrow, I was able to read Stata files into R, write the dataframe out to Feather and read the Feather file into a Pandas dataframe with no manipulation.
Without Feather I would have had to resort to using CSVs as intermediate files which would have meant additional pre-processing in R and post-processing in Pandas. Feather and Arrow saved me a bunch of time on this.
I'm looking forward to using Arrow more broadly but, even with just Feather, Wes and Hadley have vastly simplified the effort of interfacing between R and Python/Pandas. I'm also very excited to see what else comes out of their partnership at Ursa Labs: https://ursalabs.org
Clearfactr was acquired by Goldman Sachs in 2018.
It's been used internally in Goldman since then and they don't make it available outside the company unfortunately.
[1] https://www.clearfactr.com/