devin-petersohn

JeyKJey

try:
    page_response = requests.get(page_link, timeout=5)
    if page_response.status_code == 200:
        # extract
    else:
        print(page_response.status_code)
        # notify, try again
except requests.Timeout as e:
    print("It is time to timeout")
    print(str(e))

aneesh-joshi

import tensorflow as tf
import numpy as np

corpus_raw = 'He is the king . The king is royal . She is the royal  queen '

# convert to lower case
corpus_raw = corpus_raw.lower()

words = []
for word in corpus_raw.split():

shagunsodhani

Skip-Thought Vectors

Introduction

• The paper describes an unsupervised approach to train a generic, distributed sentence encoder.
• It also describes a vocabulary expansion method to encode words not seen at training time.
• Link to the paper

Skip-Thoughts

codekansas

from __future__ import print_function

import json
import os
import numpy as np

from gensim.models import Word2Vec
from gensim.utils import simple_preprocess
from keras.engine import Input
from keras.layers import Embedding, merge

myui

% 1. Title: Iris Plants Database
% 
% 2. Sources:
%      (a) Creator: R.A. Fisher
%      (b) Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)
%      (c) Date: July, 1988
% 
% 3. Past Usage:
%    - Publications: too many to mention!!!  Here are a few.
%    1. Fisher,R.A. "The use of multiple measurements in taxonomic problems"

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)

PurpleBooth

Project Title

One Paragraph of project description goes here

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

Prerequisites

destan

# coding=utf8

import PIL
from PIL import ImageFont
from PIL import Image
from PIL import ImageDraw

def text2png(text, fullpath, color = "#000", bgcolor = "#FFF", fontfullpath = None, fontsize = 13, leftpadding = 3, rightpadding = 3, width = 200):
	REPLACEMENT_CHARACTER = u'\uFFFD'
	NEWLINE_REPLACEMENT_STRING = ' ' + REPLACEMENT_CHARACTER + ' '

bradmontgomery

"""
Use a Counter to find the most common words in "The Wonderful Wizard of Oz" by
L. Frank Baum.

Available in (mostly) plain text at:
https://archive.org/stream/wonderfulwizardo00baumiala/wonderfulwizardo00baumiala_djvu.txt

Note: This code also counts the words in the header, so it's not a *realistic*
applicaton, but more of a demonstration of python's Counter.