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What's funny about this statement is I'm similar... but just recently I told the "pre script" to fuck off, then I realized I wrote exactly what was already pre-written.

So, maybe it isn't making you dumber if you think about what you want to say and then pick the one that is closest or exact.


A lot of unneeded info that is now required.

I'm surprised email and facebook doesn't demand your SSN.


Im pretty sure that a) demanding SSN/DL would cause a mass exodus that would have a bottom line impact for FB b) FB probably already have it through other data sources matched with your FB history


... Wouldn't you think that jobs should go to American citizens, before out sourcing and bringing everyones wages down?


Why would American citizenship be an indicator of deservingness?

> everyones

Why aren't foreigners in the category of "everyone"?


I'm starting to think that Prius and Leaf and other EVs are making the car look sooo bad on purpose.

The car gets the best mileage per charge in town (hybrids anyway), so "wind efficient" isn't really a thing.


I've been doing Coursera and got done with Udacity but both are just too philosophical. (I'll probably cruse the deep learning class too.)

Does FAST.AI get into the grit of actually doing or is it more philosophical?


Fast.AI is 100% focused on getting into DOING stuff. Its guiding philosophy is that teaching the math first instead of teaching you how to actually do some machine learning is backwards. The lectures start with a dozen lines of code that learns how to discriminate between cats and dogs and then follows up on that by slowly removing layers of abstraction. It's very cool.

That said, the lectures are pretty rough around the edges compared to something like a coursera class. And his goal is to get you to play around with stuff on your own using his stuff as a jumping off point. It works well for some people but not as much for others.


Very focused on getting shit done, while trying to help you build appropriate mental models to what's going on. Towards the end of the course, some more theory.

Multiple times, the prof said things like "the theory says this shouldn't work, but it does, so we use it." (Thinking about his perspective on the practical reality surrounding the "curse of dimensionality" (i.e. that it's not a curse)


They teach you how to efficiently use (two kinds of) hammers and what nails to knock

1. ConvNet (for images and structured data)

2. RNN (for text and sequence data).

They don't teach you far advanced stuff, like how to creatively misuse the hammers to knock different nails (example: using ConvNet, tweak as causal convolution to handle sequence data)


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