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:
<!DOCTYPE html> | |
<html> | |
<head> | |
<meta name="description" content="Speech Synthesis API demo by Aurelio De Rosa (@AurelioDeRosa)" /> | |
<meta charset="utf-8"> | |
<title>Speech Synthesis API demo</title> | |
<style id="jsbin-css"> | |
.text-to-speech { | |
float: right; | |
cursor: pointer; |
<!DOCTYPE html> | |
<html> | |
<head> | |
<meta name="description" content="This is the form demo of the talk Talking and listening to web pages" /> | |
<meta charset="utf-8"> | |
<title>Form demo</title> | |
<style id="jsbin-css"> | |
body | |
{ | |
font-size: 24px; |
'''This script goes along the blog post | |
"Building powerful image classification models using very little data" | |
from blog.keras.io. | |
It uses data that can be downloaded at: | |
https://www.kaggle.com/c/dogs-vs-cats/data | |
In our setup, we: | |
- created a data/ folder | |
- created train/ and validation/ subfolders inside data/ | |
- created cats/ and dogs/ subfolders inside train/ and validation/ | |
- put the cat pictures index 0-999 in data/train/cats |
'''This script goes along the blog post | |
"Building powerful image classification models using very little data" | |
from blog.keras.io. | |
It uses data that can be downloaded at: | |
https://www.kaggle.com/c/dogs-vs-cats/data | |
In our setup, we: | |
- created a data/ folder | |
- created train/ and validation/ subfolders inside data/ | |
- created cats/ and dogs/ subfolders inside train/ and validation/ | |
- put the cat pictures index 0-999 in data/train/cats |
##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
'''This script goes along the blog post | |
"Building powerful image classification models using very little data" | |
from blog.keras.io. | |
It uses data that can be downloaded at: | |
https://www.kaggle.com/c/dogs-vs-cats/data | |
In our setup, we: | |
- created a data/ folder | |
- created train/ and validation/ subfolders inside data/ | |
- created cats/ and dogs/ subfolders inside train/ and validation/ | |
- put the cat pictures index 0-999 in data/train/cats |
/* | |
##Device = Desktops | |
##Screen = 1281px to higher resolution desktops | |
*/ | |
@media (min-width: 1281px) { | |
/* CSS */ | |