rxaviers

People

:bowtie:	😄 :smile:	😆 :laughing:
😊 :blush:	😃 :smiley:	☺️ :relaxed:
😏 :smirk:	😍 :heart_eyes:	😘 :kissing_heart:
😚 :kissing_closed_eyes:	😳 :flushed:	😌 :relieved:
😆 :satisfied:	😁 :grin:	😉 :wink:
😜 :stuck_out_tongue_winking_eye:	😝 :stuck_out_tongue_closed_eyes:	😀 :grinning:
😗 :kissing:	😙 :kissing_smiling_eyes:	😛 :stuck_out_tongue:

xk

/* gcc -std=c99 -o brightness brightness.c -framework IOKit -framework ApplicationServices */

#include <stdio.h>
#include <unistd.h>
#include <IOKit/graphics/IOGraphicsLib.h>
#include <ApplicationServices/ApplicationServices.h>

const int kMaxDisplays = 16;
const CFStringRef kDisplayBrightness = CFSTR(kIODisplayBrightnessKey);
const char *APP_NAME;

losisli

测试平台：DigitalOcean VPS ubuntu14.04 x64, strongswan5.2.2

运行以下命令请使用root权限

一：安装strongswan

由于ubuntu软件仓库中strongswan版本较低，因此从官网源码编译安装

apt-get install build-essential     #编译环境
aptitude install libgmp10 libgmp3-dev libssl-dev pkg-config libpcsclite-dev libpam0g-dev     #编译所需要的软件

paulochf

from IPython.display import display, HTML

display(HTML(data="""
<style>
    div#notebook-container    { width: 95%; }
    div#menubar-container     { width: 65%; }
    div#maintoolbar-container { width: 99%; }
</style>
"""))

baraldilorenzo

##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

MarcWang

How to install OpenCV 3.1 on Ubuntu 14.04 64bits

Update latest packages and installed

$ sudo apt-get update
$ sudo apt-get upgrade

apt-get update - 更新最新的套件資訊 apt-get upgrade - 更新套件

zezba9000

#include "stdafx.h"
#include <Windows.h>
#include <iostream>
#include <string>
using namespace std;

class Base
{
public:
	virtual void foo(int i)

fchollet

'''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

fchollet

'''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

fchollet

'''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