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)

dergachev

OS X Screencast to animated GIF

This gist shows how to create a GIF screencast using only free OS X tools: QuickTime, ffmpeg, and gifsicle.

Instructions

To capture the video (filesize: 19MB), using the free "QuickTime Player" application:

bastman

Docker - How to cleanup (unused) resources

Once in a while, you may need to cleanup resources (containers, volumes, images, networks) ...

delete volumes

// see: https://github.com/chadoe/docker-cleanup-volumes

$ docker volume rm $(docker volume ls -qf dangling=true)

$ docker volume ls -qf dangling=true | xargs -r docker volume rm

htr3n

Built-in

diskutil erasevolume HFS+ 'RAM Disk' `hdiutil attach -nobrowse -nomount ram://XXXXX`

where XXXXX is the size of the RAM disk in terms of memory blocks.

Notes:

karpathy

""" 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

marktheunissen

This playbook has been removed as it is now very outdated.

lttlrck

git branch -m old_branch new_branch         # Rename branch locally    
git push origin :old_branch                 # Delete the old branch    
git push --set-upstream origin new_branch   # Push the new branch, set local branch to track the new remote

graydon

# extracted from http//www.naturalearthdata.com/download/110m/cultural/ne_110m_admin_0_countries.zip
# under public domain terms

country_bounding_boxes = {
    'AF': ('Afghanistan', (60.5284298033, 29.318572496, 75.1580277851, 38.4862816432)),
    'AO': ('Angola', (11.6400960629, -17.9306364885, 24.0799052263, -4.43802336998)),
    'AL': ('Albania', (19.3044861183, 39.624997667, 21.0200403175, 42.6882473822)),
    'AE': ('United Arab Emirates', (51.5795186705, 22.4969475367, 56.3968473651, 26.055464179)),
    'AR': ('Argentina', (-73.4154357571, -55.25, -53.628348965, -21.8323104794)),
    'AM': ('Armenia', (43.5827458026, 38.7412014837, 46.5057198423, 41.2481285671)),

amatellanes

/* Useful celery config.

app = Celery('tasks',
             broker='redis://localhost:6379',
             backend='redis://localhost:6379')

app.conf.update(
    CELERY_TASK_RESULT_EXPIRES=3600,
    CELERY_QUEUES=(
        Queue('default', routing_key='tasks.#'),

njanakiev

import os
import random
from PIL import Image, ImageOps


def concat_images(image_paths, size, shape=None):
    # Open images and resize them
    width, height = size
    images = map(Image.open, image_paths)
    images = [ImageOps.fit(image, size, Image.ANTIALIAS)