Talk delivered 2015-07-29 at ICERM workshop on "mathematics and data science"
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The following recipes are sampled from a trained neural net. You can find the repo to train your own neural net here: https://github.com/karpathy/char-rnn Thanks to Andrej Karpathy for the great code! It's really easy to setup.
The recipes I used for training the char-rnn are from a recipe collection called ffts.com And here is the actual zipped data (uncompressed ~35 MB) I used for training. The ZIP is also archived @ archive.org in case the original links becomes invalid in the future.
Magic words:
psql -U postgresSome interesting flags (to see all, use -h or --help depending on your psql version):
-E: will describe the underlaying queries of the\commands (cool for learning!)-l: psql will list all databases and then exit (useful if the user you connect with doesn't has a default database, like at AWS RDS)
Note: I'm currently taking a break from this course to focus on my studies so I can finally graduate
In penance for cracking stupid jokes on Twitter, here's my Emacs cheat sheet. Emacs has a steep learning curve, so I've tried to order them by importance so you could learn them in stages.
One overall rule of thumb: pay attention to the minibuffer (the line at the bottom of the editor). It will often guide you through a process, and also gives you hints about what state you're in, such as the middle of a multi-chord sequence.
The other rule of thumb: when in doubt, C-g it out.
You simply can't get by without having these at your fingertips.