johannes-z

import Vue from 'vue'

/**
 * Usage:
 * <span v-slot:one="{ slots, value: 'component B2' }"></span>
 * v-slot:<SLOT_NAME>="{ slots, <VALUE> }"
 *
 * Pass the slots like this:
 * <my-component :slots="$scopedSlots" />
 * inside MyComponent define props: ['slots']

DavidWells

Aligning images

This is a guide for aligning images.

See the full Advanced Markdown doc for more tips and tricks

left alignment

qgp9

#include <string>
#include <iostream>
#include <sstream>
#include <iomanip>
#include <stdlib.h> 
class SafeFormatter{
public:
  SafeFormatter( const char * s ):fForm(s),fIterator(fForm.begin()){}
  void compile(){return ;}
  template< typename T, typename ... A >

qgp9

#include <iostream>
#include <vector>
#include <algorithm>
class Ranger {
public:
  typedef int value_type;
  struct iterator {
    iterator(size_t counter) : counter(counter) {}
    iterator operator++(){ return iterator(++counter); }
    bool operator!=(iterator o) { return counter != o.counter; }

qgp9

• 1-specialization-static.cxx
• 2-specialization-module-simple.cxx
• 3-specialization-module-full.cxx
• 4-specialization-Inherit.cxx
• z-result.txt

qgp9

#include <iostream>
#include <vector>


//=====================================================
// V1 : Wraper class of vector ( has vector ) 
//=====================================================
template < class T , int dim > 
class MultiVector {
public:

qgp9

How to make a tumbnail of snapshots from a movie clip with libAV

haje01

도커와 AWS를 활용한 클라우드 딥러닝 환경 구축

글쓴이: 김정주(haje01@gmail.com)

최근 딥러닝 관련 패키지들은 대부분 CPU와 GPU를 함께 지원하고 있습니다. GPU를 사용하면 보다 빠르게 학습 결과를 낼 수 있지만, GPU를 활용하기 위해서는 NVIDIA계열의 그래픽 카드, 드라이버 S/W 그리고 CUDA의 설치를 필요로 합니다.

이 글에서는 AWS의 GPU 인스턴스와 도커를 활용해 딥러닝 패키지(Caffe)를 편리하게 사용하는 방법을 소개합니다.

---

qgp9

template < class T, int n > struct mvector_tool{
  typedef vector< typename mvector_tool<T,n-1>::type > type;
  static type gen_vector( std::array<unsigned int, n> index, T initvalue ){
    std::array<unsigned int,n-1> index_next;
    std::copy_n( index.begin()+1, n-1, index_next.begin() );
    return type( index.front(), mvector_tool<T,n-1>::gen_vector( index_next, initvalue ));
  }
};

template < class T > struct mvector_tool<T,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)