This gist shows how to create a GIF screencast using only free OS X tools: QuickTime, ffmpeg, and gifsicle.
To capture the video (filesize: 19MB), using the free "QuickTime Player" application:
// by dave @ beesandbombs.tumblr.com >:) | |
void setup() { | |
setup_(); | |
result = new int[width*height][3]; | |
result_ = new int[width*height][3]; | |
} | |
int[][] result, result_; | |
float time; |
Your monthly notice to go through and disconnect unused applications across various services. | |
Twitter: https://twitter.com/settings/applications | |
Dropbox: https://www.dropbox.com/account/connected_apps | |
Facebook: https://www.facebook.com/settings?tab=applications | |
Google Apps: https://security.google.com/settings/security/permissions?pli=1 | |
Google Ads: http://www.google.com/ads/preferences | |
Google Account: https://security.google.com/settings/security/secureaccount | |
Foursquare: https://foursquare.com/settings/connections | |
Instagram: https://instagram.com/accounts/manage_access |
See comments section for more up-to-date versions of the script. The original script is from 2014 and will not work as is.
Facebook will block this feature for you while you use it, depending on how much entities you try to unfollow. It automatically unblocks in a couple of hours and you will be able to continue.
""" | |
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) |
import requests | |
import urllib | |
import requests | |
import json | |
import random | |
""" | |
here is a wrapper for the *unreleased* electric objects API | |
Built by Harper Reed ([email protected]) - @harper |
##VGG16 model for Keras
This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.
It has been obtained by directly converting the Caffe model provived by the authors.
Details about the network architecture can be found in the following arXiv paper:
Very Deep Convolutional Networks for Large-Scale Image Recognition
K. Simonyan, A. Zisserman
The following instructions are for creating your own animations using the style transfer technique described by Gatys, Ecker, and Bethge, and implemented by Justin Johnson. To see an example of such an animation, see this video of Alice in Wonderland re-styled by 17 paintings.
The easiest way to set up the environment is to simply load Samim's a pre-built Terminal.com snap or use another cloud service like Amazon EC2. Unfortunately the g2.2xlarge GPU instances cost $0.99 per hour, and depending on parameters selected, it may take 10-15 minutes to produce a 512px-wide image, so it can cost $2-3 to generate 1 sec of video at 12fps.
If you do load the
This is just a quick list of resourses on TDA that I put together for @rickasaurus after he was asking for links to papers, books, etc on Twitter and is by no means an exhaustive list.
Both Carlsson's and Ghrist's survey papers offer a very good introduction to the subject
Mapper
algorithm.