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@MohamedAlaa
MohamedAlaa / tmux-cheatsheet.markdown
Last active November 17, 2024 01:28
tmux shortcuts & cheatsheet

tmux shortcuts & cheatsheet

start new:

tmux

start new with session name:

tmux new -s myname
@nylki
nylki / char-rnn recipes.md
Last active August 26, 2024 01:05
char-rnn cooking recipes

do androids dream of cooking?

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.

@karpathy
karpathy / min-char-rnn.py
Last active November 16, 2024 23:10
Minimal character-level language model with a Vanilla Recurrent Neural Network, in Python/numpy
"""
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)
@karpathy
karpathy / pg-pong.py
Created May 30, 2016 22:50
Training a Neural Network ATARI Pong agent with Policy Gradients from raw pixels
""" 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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@oxinabox
oxinabox / day1.dx
Last active December 14, 2020 22:37
Advent of Code 2020, in DexLang
'# Advent of Code 2020. Day 1
DexLang, [Lyndon White](http://oxinabox.net)
## part 1
From the list find two entries that sum to 2020, and compute their product.
list = [1567, 1223, 1758, 1842, 1933, 1898, 1409, 1058, 1533, 1417, 1032, 1634, 1477, 1394, 1888, 1972, 1237, 1390, 1677, 1546, 1302, 1070, 1369, 1455, 1065, 1924, 1593, 1131, 1064, 1346, 1914, 1129, 1830, 1450, 1278, 1740, 1809, 1176, 1734, 1102, 1807, 1982, 1603, 1736, 2008, 1980, 1905, 1633, 1732, 1350, 1865, 1988, 1805, 1998, 1152, 1046, 1870, 1557, 1789, 1766, 1945, 1359, 1002, 1126, 1719, 1497, 1296, 1560, 1936, 1929, 1464, 2005, 1281, 618, 1257, 1107, 1632, 1688, 1964, 1803, 1360, 1384, 1889, 1411, 1328, 1452, 1868, 1515, 1586, 1631, 1618, 1087, 1710, 1094, 1774, 1295, 1700, 1636, 1230, 1421, 1910, 1522, 1366, 1144, 1757, 1493, 1316, 1103, 687, 1371, 1720, 1155, 1559, 1900, 989, 1367, 1999, 1066, 1773, 1787, 1402, 1047, 1806, 1956, 1219, 1555, 1307, 1419, 1706, 1884, 1109, 1181, 2010, 1298, 1730, 1078, 1848, 1398, 1687, 2007, 1550, 1664, 1225
@theogf
theogf / mondrian.jl
Last active March 16, 2021 09:03
Sample code for generating a Mondrian process and plotting it
using CairoMakie
# using Animations
using Distributions
using Random
using AbstractTrees
CairoMakie.activate!()
mutable struct Partition
x
y
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from __future__ import annotations
from contextlib import contextmanager
from typing import NamedTuple, Callable, Optional, Any
import numpy as np
Array = Any
class Node(NamedTuple):
vjp: Optional[Callable]
parents: List[Node]