It's great to see work being done to highlight an issue but I do wonder what background does the author have? Would recommend gestalt/cleveland as a good grounding, the visualizations is editorial rather than analytical.
Choosing US versus Japan, which Japan has the lowest cost and highest life expectancy in the OECD, it's cherry picking. I'd recommend showing the full distribution of OECD per-capita spending rather than just a single cherry picked comparison.
This also is troubled by McNamara Fallacy, we're looking at metrics that are qunatifiable but ignoring what can't be measured or overlooked, is speed of access being considered, how about innovation incentives, quality and outcomes variation across systems, patient choice and flexibility, in addition to workforce compensation (nurses and physicians in the US earn significantly more). Where are the trade-offs?
Summary Statistics can be dangerous. 254% of medicare is a single ratio summarizing enormous variation across thousands of hospitals and procedures. Median markup of 3.96x inherently hides the distribution, some hopsitals may be higher or lower, why is that?
I think the biggest one to me was the confirmation bias, the $3 trillion gap that represented 'fixable waste' was the conclusion. Every price difference is interpreted as waste rather than investigating the potential cost drivers, was there a null finding framework in place where US spending appears justified or is it all bad?
Overall, glad someone is looking into the data and pulling insights, please don't take this as discouragement just a comment from the peanut gallery.
This does an honest good job of walking through the beginnings, I would still say understanding/decomposing a decision tree and going through the details and choices /trade offs one makes with how they prepare the tree like binary split or discrete/binning for continuous data. What reducing entropy means, etc. Maybe even start with parametric versus nonparametric modeling pros/cons. You really get to see how probability and statistics is applied in the formulas that eventually will be thrown into a dot function in python.
There is a lot of content on pytorch, which is great and makes a ton of sense since it's used so heavily, where the industry needs a ton of help/support in is really the fundamentals. Nonetheless, great contribution!
I purchased this as soon as it was announced, I was surprised they had it ready to ship on the day of the CES announcement.
I do enjoy it, with Fancyzones, I can set up Unreal Engine Editor, Rider, discord/teams and a small corner window for searching and/or youtube watching on the side. At first I thought the pixel count was going to be too low but from my position it 'feels' retina at 125% windows scaling. Yes you can do the same with multiple monitors but I don't get the fatigue of turning my physical head, it's the perfect size to sit in middle and use your eyes to adjust/focus if that makes sense..
120hz and fast motion helps a lot. DCS World looks amazing on this, it feels like it's your full fov when playing games. Granted this isn't an OLED panel, I wouldn't play anything competitive on here but EU V and/or RTS games are very nice at 6k/52.
This replaced my dual 4K 120hz monitors. Recommend if you're not gaming.
I learned on the MIPS processor, computer organization / architecture one of the most challenging CS courses for me. I don't remember much but I definitely remember the mips pipeline...
Choosing US versus Japan, which Japan has the lowest cost and highest life expectancy in the OECD, it's cherry picking. I'd recommend showing the full distribution of OECD per-capita spending rather than just a single cherry picked comparison.
This also is troubled by McNamara Fallacy, we're looking at metrics that are qunatifiable but ignoring what can't be measured or overlooked, is speed of access being considered, how about innovation incentives, quality and outcomes variation across systems, patient choice and flexibility, in addition to workforce compensation (nurses and physicians in the US earn significantly more). Where are the trade-offs?
Summary Statistics can be dangerous. 254% of medicare is a single ratio summarizing enormous variation across thousands of hospitals and procedures. Median markup of 3.96x inherently hides the distribution, some hopsitals may be higher or lower, why is that?
I think the biggest one to me was the confirmation bias, the $3 trillion gap that represented 'fixable waste' was the conclusion. Every price difference is interpreted as waste rather than investigating the potential cost drivers, was there a null finding framework in place where US spending appears justified or is it all bad?
Overall, glad someone is looking into the data and pulling insights, please don't take this as discouragement just a comment from the peanut gallery.