razzledazze

from turtle import Turtle
from random import randint

grass = Turtle()
grass.color('#007b0c')

sky = Turtle()
sky.color('#e5e5ff')
sky.pensize(100)
sky.speed(0)

stared

nzw0301

*2vec papers

1. act2vec, trace2vec, log2vec, model2vec https://link.springer.com/chapter/10.1007/978-3-319-98648-7_18
2. apk2vec https://arxiv.org/abs/1809.05693
3. app2vec http://paul.rutgers.edu/~qma/research/ma_app2vec.pdf
4. ast2vec https://arxiv.org/abs/2103.11614
5. attribute2vec https://arxiv.org/abs/2004.01375
6. author2vec http://dl.acm.org/citation.cfm?id=2889382
7. baller2vec https://arxiv.org/abs/2102.03291
8. bb2vec https://arxiv.org/abs/1809.09621

napsternxg

"""
Author: Shubhanshu Mishra
Posted this on the keras issue tracker at: https://github.com/fchollet/keras/issues/108
Implementing a linear regression using Keras. 
"""

from keras.models import Sequential
from keras.layers.core import Dense, Activation

model = Sequential()

karpathy

"""
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)

mreid

# Example Huffman coding implementation
# Distributions are represented as dictionaries of { 'symbol': probability }
# Codes are dictionaries too: { 'symbol': 'codeword' }

def huffman(p):
    '''Return a Huffman code for an ensemble with distribution p.'''
    assert(sum(p.values()) == 1.0) # Ensure probabilities sum to 1

    # Base case of only two symbols, assign 0 or 1 arbitrarily
    if(len(p) == 2):

MohamedAlaa

tmux shortcuts & cheatsheet

start new:

tmux

start new with session name:

tmux new -s myname