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korymath /
Created May 13, 2019 — forked from dannguyen/
Using youtube-dl and gifify from the command-line
korymath / gist:1c1c4a8a7aabce4b1882cbfc7906b079
Created Jun 1, 2018 — forked from guisehn/gist:6648c8fdcd1102a22a22
Backup Heroku Postgres database and restore to local database
View gist:1c1c4a8a7aabce4b1882cbfc7906b079

Grab new backup

Command: heroku pg:backups capture -a [app_name]


Command: curl -o latest.dump `heroku pg:backups public-url -a [app_name]`

Restore backup dump into local db

korymath /
Created Apr 30, 2018 — forked from duncdrum/
dark theme for Texshop
# Safari Reader Night Theme
# by @LogicaEns
# background = 39 40 34 (#272822)
defaults write TeXShop background_R 0.05
defaults write TeXShop background_G 0.06
defaults write TeXShop background_B 0.03
# commands = 102 217 239 (#66D9EF)
defaults write TeXShop commandred 0.3
import random
import itertools
import numpy as np
from tqdm import tqdm
# 0) define the starting set
num_students = 20
student_ids = np.arange(num_students)
all_combinations = list(itertools.combinations(student_ids, 2))
# Exit on error #
set -e
# Clean up #
rm -rf ~/programs/libevent
rm -rf ~/programs/ncurses
rm -rf ~/programs/tmux
# Variable version #
View TAL on AWS
python --data_dir data/TAL --rnn_size 1024 --num_layers=3 --batch_size=128 --seq_length=256 --input_keep_prob 0.8 --output_keep_prob 0.5
korymath /
Created Mar 16, 2017
Running NAO simulations on EC2
# The people who are crazy enough to think they can change the world are the ones who do
View gist:38c0d88e021f8e9f13e231fc637f125f
View torch-twrl policy gradient
Following the installation and first test for torch-twrl
Run code:
cd examples
chmod u+x
korymath /
Last active Aug 23, 2016
Quick n-gram discovery on the NIPS 2016 accepted papers, from
from sklearn.feature_extraction.text import CountVectorizer
from collections import Counter
import numpy as np
with open('accepted-papers.txt', 'r') as myfile:'\n', '')
lectures = data
bigram_vectorizer = CountVectorizer(ngram_range=(2, 10), stop_words='english')
analyze = bigram_vectorizer.build_analyzer()
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