Churchill also took a 2 hour nap daily. The man would change into his damn pajamas to do it. Night time sleep is not total sleep. Let's stop glorifying folks who supposedly need so little sleep...they may be getting it other ways.
I wrote a technical book for Wiley. 12.5% royalties for paper sales. 25% for book. Negotiate the ebook rate.
The main thing that surprised me (which shouldn't have) was the complete lack of marketing Wiley did. You write it, you find the tech editors, you edit it. They assign someone to you who basically bugs you to turn in chapters. I had the cover designed myself so it wouldn't suck. Then it comes out and you are the one who has to market it. but for me it was fun just to do it.
Is that 12.5% of the list price or 12.5% of the profits?
E.g. if the list price $100, the publisher sells it wholesale for $45 and the printing cost is $10, will your royalty be $12.50 or $4.37=(12.5% of $35)?
All I know is that I spent two years deep in abstract algebra and other proof-heavy classes, and it was some of the best training in rigor and thoughtfulness I've ever had. Wouldn't trade it for the world. But, then again, most recent grads weren't math majors. I think my university of 30k graduated about 15 math majors a year.
I used to have a breadfruit tree in my yard in Tennessee. We would take the fruit as it fell and line it up in the street and watch cars run over them. Seeing them crushed was pretty satisfying (I was 9 years old at the time).
Then one day a car lost traction on our breadfruit gauntlet and the driver got terribly pissed. Haven't played with the fruit since.
This has not improved. However, instead of using screen in a terminal to do a long create table query, I'll now just kick it off in pgAdmin and yank my ethernet. This causes the query to just hang out there indefinitely until it's done. Stupid, yes. But it's kinda fun.
Yeah. Autism is diagnosed by symptoms and the symptoms have changed over time. It used to be that an individual had to actively avoid social contact. Now they just need to be socially impaired even if they seek contact. As the symptoms have changed and as awareness has increased in the medical community, so have diagnoses increased.
I agree that it's not the insane uptick that the raw statistics would seem to indicate. But disputing that fact doesn't negate the fact that 1 in 68 kids has some form of disability (not saying you did... but I have seen that from some people as reason to dismiss the issue).
I actually don't care one bit about the diagnosis itself. It's not as if having the diagnosis means you can take a pill to get rid of it. The only benefit we got from a diagnosis came from insurance paying for certain claims they previously would not have.
And among those 1 in 68 are people whose brilliance in extricably tied to that disability -- including most of the greatest scientists of past and present.
Hey, author here. It does sound kinda bigoted, doesn't it? Lemme clarify. I would really love for more people to learn this stuff. Truly. That's why I wrote a book to teach it to folks in Excel. What freaks me out a bit is that in order to enlarge the ML pie, a lot of vendors are trying to democratize machine learning not by teaching it to more people but by putting the gun in hands of folks who don't actually understand how supervised machine learning works and where it can go wrong.
I don't think people need PhDs to learn or do this stuff. But I think they need more than a "machine learning made easy!" app and a gung-ho attitude.
I feel that I'm halfway between main street and an ivory tower...not sure where that puts me. The upstairs bar at a pizza parlor?
Speaking as one of the people that phrase is probably pointed at, I don't begrudge the choice of words at all.
Lately there's been a big thrust in my industry to do just what you're worried about, and now that I've started to get an inkling of what's going on under the hood with predictive modeling tools over the past couple years it's starting to worry me.
And I'm starting to find their sales pitches to be even more condescending. "Don't worry about how this works or what it's really doing. Just pretend it's magic." It doesn't speak well for the vendors' opinion of their customers, and it leaves a lot of room open for legitimate technology to morph into silicon snake oil.
I just recently posted in a HN machine learning thread asking for beginners resources. This sounds right up my alley.
I'm teaching myself Ruby (and other stuff), but consider myself pretty advanced with Excel and web analytics in general. This seems like a great way for me to get my feet wet in the deeper science of things with tools I'm already very familiar with (moreso than Ruby at least).
John, can you clarify a bit on how much background is needed in various areas of math to get the most out of this book? Or do you feel you do a solid job of teaching that as one progresses through the chapters?
A semester of linear algebra (or just a willingness to Wikipedia a few things) plus Excel experience is all you need.
That said, the book does require a lot of effort, because the techniques are worked through step by step.
But once you learn all the guts of the algorithms, you never have to implement them again! The last chapter moves the reader into R package land with the confidence that you now know what those packages are basically doing and what to watch out for.
If you've got a college semester of linear algebra under your belt (or equivalent) and are pretty good with Excel, then the book is a good fit. Even the algebra can be optional if you're willing to use wikipedia liberally. I don't take for granted that the reader has a lot of background.
That said, there are parts in the book that are really quite hard. Hard in that they just take time to work through. Because the book is about learning all the steps that go into training models and doing analyses from scratch. But once you do it all from scratch once, you don't necessarily have to ever ever do it again.
It's taught in Excel for learning purposes, and then the last chapter moves you into R. Literally, the Holt Winters forecasting chapter of the book is 50 pages while in R it's the forecast package plus 3 lines of code.
Combining =index() and =match() seems to be less computationally intensive than =vlookup() -- give it a shot.
I've had entire days spent copying formulas down 5000 rows (x20-50 cols), copy, paste-values, and repeat. Can't wait to try this out, just signed up for the beta.
Another thought -- as someone who runs windows on a VM on my mac almost purely for excel purposes, this could enable me to go 100% mac. The biggest downfall to mac's excel seems to be a computation engine that's far behind the windows version.
Sounds more like a classic optimization model (operations research) than like an AI model. Great case study though. I wonder why they used a GA and custom stuff instead of off the shelf software from gurobi or IBM OPL etc.
For scheduling problems, I have had way more luck with open source CP solvers like Choco than I have had with hyper-optimized commercial IP solvers like gurobi. Branch and Cut is just too indirect for constraint heavy IP models.
"Nurse scheduling competitions" seem to alternate between IP and CP approaches - my feeling is that when the number of solutions is "small", CP pulls ahead, but when there are many solutions and the objective is important, IP wins. Very case-by-case dependent, regardless.
I've found that size doesn't matter much at all...but constraint type does (hard vs soft). Soft constraints very easily lend themselves to IP formulations, but problems with lots of hard constraints require a ton of luck to get it to work quickly with an IP solver. 3d bin packing, for example, is very heavy on hard constraints. I have a CP model (using the Geost constraint primarily) with an ensemble search heuristic (genetic + LNS), and I have yet to find an IP model that can get within 2 orders of magnitude of the average solve times of the CP model.
