shagunsodhani

Pixel Recurrent Neural Network

Introduction

• Problem: Building an expressive, tractable and scalable image model which can be used in downstream tasks like image generation, reconstruction, compression etc.
• Link to the paper

Model

• Scan the image, one row at a time and one pixel at a time (within each row).

anaisbetts

// Include this at the very top of both your main and window processes, so that
// it loads as soon as possible.
//
// Why does this work? The node.js module system calls fs.realpathSync a _lot_
// to load stuff, which in turn, has to call fs.lstatSync a _lot_. While you
// generally can't cache stat() results because they change behind your back
// (i.e. another program opens a file, then you stat it, the stats change),
// caching it for a very short period of time is :ok: :gem:. These effects are
// especially apparent on Windows, where stat() is far more expensive - stat()
// calls often take more time in the perf graph than actually evaluating the

ashander

// 1. Go to https://twitter.com/i/likes
// 2. Keep scrolling to the bottom repeatedly until all your favs are loaded.
// 3. Run this in your console (open in chrome by View > Developer > JavaScript Console)
// Notes: this may take a while if you have a lot of favs/likes
//       you can only access your most recent ~2000 likes.
//       inspired by https://gist.github.com/JamieMason/7580315
$('.ProfileTweet-actionButtonUndo').click()

eevee

"""Perlin noise implementation."""
# Licensed under ISC
from itertools import product
import math
import random


def smoothstep(t):
    """Smooth curve with a zero derivative at 0 and 1, making it useful for
    interpolating.

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',

Hello1024

#!/bin/bash

# Run this on This AMI on AWS:
# https://console.aws.amazon.com/ec2/v2/home?region=us-east-1#LaunchInstanceWizard:ami=ami-b36981d8

# You should get yourself a fully working GPU enabled tensorflow installation.

cd ~

# grab cuda 7.0

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)

justinweiss

# Call scopes directly from your URL params:
#
#     @products = Product.filter(params.slice(:status, :location, :starts_with))
module Filterable
  extend ActiveSupport::Concern

  module ClassMethods
    
    # Call the class methods with names based on the keys in <tt>filtering_params</tt>
    # with their associated values. For example, "{ status: 'delayed' }" would call 

yetithefoot

{url:'stun:stun01.sipphone.com'},
{url:'stun:stun.ekiga.net'},
{url:'stun:stun.fwdnet.net'},
{url:'stun:stun.ideasip.com'},
{url:'stun:stun.iptel.org'},
{url:'stun:stun.rixtelecom.se'},
{url:'stun:stun.schlund.de'},
{url:'stun:stun.l.google.com:19302'},
{url:'stun:stun1.l.google.com:19302'},
{url:'stun:stun2.l.google.com:19302'},

willurd

Each of these commands will run an ad hoc http static server in your current (or specified) directory, available at http://localhost:8000. Use this power wisely.

Discussion on reddit.

Python 2.x

$ python -m SimpleHTTPServer 8000