| Key | Result |
|---|---|
v |
select |
y |
copy (yank) |
c |
change |
d |
delete |
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| #!/usr/bin/env python | |
| import sys | |
| import argparse | |
| import re | |
| import subprocess | |
| def main(requirements_in, interactive=False): | |
| required = {} |
I liked the way Grokking the coding interview organized problems into learnable patterns. However, the course is expensive and the majority of the time the problems are copy-pasted from leetcode. As the explanations on leetcode are usually just as good, the course really boils down to being a glorified curated list of leetcode problems.
So below I made a list of leetcode problems that are as close to grokking problems as possible.
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| import re | |
| # http://stackoverflow.com/a/13752628/6762004 | |
| RE_EMOJI = re.compile('[\U00010000-\U0010ffff]', flags=re.UNICODE) | |
| def strip_emoji(text): | |
| return RE_EMOJI.sub(r'', text) | |
| print(strip_emoji('🙄🤔')) |
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| """ Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """ | |
| import numpy as np | |
| import cPickle as pickle | |
| import gym | |
| # hyperparameters | |
| H = 200 # number of hidden layer neurons | |
| batch_size = 10 # every how many episodes to do a param update? | |
| learning_rate = 1e-4 | |
| gamma = 0.99 # discount factor for reward |
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| """ | |
| Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy) | |
| BSD License | |
| """ | |
| import numpy as np | |
| # data I/O | |
| data = open('input.txt', 'r').read() # should be simple plain text file | |
| chars = list(set(data)) | |
| data_size, vocab_size = len(data), len(chars) |
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| /* | |
| * I add this to html files generated with pandoc. | |
| */ | |
| html { | |
| font-size: 100%; | |
| overflow-y: scroll; | |
| -webkit-text-size-adjust: 100%; | |
| -ms-text-size-adjust: 100%; | |
| } |