kalefranz

Descriptors

• http://nedbatchelder.com/blog/201306/explaining_descriptors.html
• http://intermediatepythonista.com/classes-and-objects-ii-descriptors

Metaclasses

aparrish

Writing Poetry with Procedure: A Workshop

Led by Allison Parrish

Description

Practitioners in the field of procedural writing have been using rules, procedures and computer programs to create innovative literary work since the invention of the digital computer. Far from the bland imitation evoked by the phrase "computer-generated poetry," these techniques facilitate the creation of work with aesthetic and emotional affordances sometimes difficult to achieve through conventional compositional techniques: serendipitous beauty, precisely imitative satire, vertiginous wonder at the infinite. In this workshop, participants will learn about the history of computer-generated writing and sample a range of off-the-shelf, freely-available tools on the web to create their own—without writing any actual lines of code. No previous programming experience is required.

Outline

danijar

# Example for my blog post at:
# https://danijar.com/introduction-to-recurrent-networks-in-tensorflow/
import functools
import sets
import tensorflow as tf


def lazy_property(function):
    attribute = '_' + function.__name__

rauchg

require-from-twitter

Since Twitter doesn't have an edit button, it's a suitable host for JavaScript modules.

How to use

Source tweet: https://twitter.com/rauchg/status/712799807073419264

const leftPad = await requireFromTwitter('712799807073419264');

yrevar

{0: 'tench, Tinca tinca',
 1: 'goldfish, Carassius auratus',
 2: 'great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias',
 3: 'tiger shark, Galeocerdo cuvieri',
 4: 'hammerhead, hammerhead shark',
 5: 'electric ray, crampfish, numbfish, torpedo',
 6: 'stingray',
 7: 'cock',
 8: 'hen',
 9: 'ostrich, Struthio camelus',

mitallast

+--------+--------------------+-----+--------------------+--------------------+--------------------+--------------------+----------+
|category|                text|label|               words|            features|       rawPrediction|         probability|prediction|
+--------+--------------------+-----+--------------------+--------------------+--------------------+--------------------+----------+
|    3001|Плойки и наборы V...| 24.0|[плойки, и, набор...|(10000,[326,796,1...|[-174.67716870697...|[6.63481663197049...|      24.0|
|     833|"Чехол-обложка дл...|  1.0|["чехол-обложка, ...|(10000,[514,986,1...|[-379.37151502387...|[5.32678001676623...|       1.0|
|     833|"Чехол-обложка дл...|  1.0|["чехол-обложка, ...|(10000,[514,986,1...|[-379.84825219376...|[2.15785456821554...|       1.0|
|     833|"Чехол-обложка дл...|  1.0|["чехол-обложка, ...|(10000,[290,514,9...|[-395.42735009477...|[6.44323423370500...|       1.0|
|     833|"Чехол-обложка дл...|  1.0|["чехол-обложка, ...|(10000,[290,514,9...|[-396.10251348

sfujiwara

# -*- coding: utf-8 -*-

import numpy as np
from scipy.sparse.linalg import cg
import tensorflow as tf
import time


def conjugate_grad(A, b, x=None):
    """

arturmkrtchyan

curl http://spark-cluster-ip:6066/v1/submissions/status/driver-20151008145126-0000

ahmetalsan

import re
import math
from collections import Counter


def get_cosine(vec1, vec2):
    intersection = set(vec1.keys()) & set(vec2.keys())
    numerator = sum([vec1[x] * vec2[x] for x in intersection])

    sum1 = sum([vec1[x]**2 for x in vec1.keys()])

yusugomori

# -*- coding: utf-8 -*-

import sys
import numpy

numpy.seterr(all='ignore')


'''