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@voluntas
voluntas / death_march.md
Last active December 29, 2023 15:36
デスマーチが起きる理由 - 3つの指標

デスマーチが起きる理由 - 3つの指標

著者: 青い鴉(ぶるくろ)さん @bluecrow2

これは結城浩さんの運用されていた YukiWiki に当時 Coffee 様 (青い鴉(ぶるくろ)さん)がかかれていた文章です。 ただ 2018 年 3 月 7 日に YukiWiki が運用停止したため消えてしまいました。その記事のバックアップです。

今は 404 ですが、もともとの記事の URL は http://www.hyuki.com/yukiwiki/wiki.cgi?%A5%C7%A5%B9%A5%DE%A1%BC%A5%C1%A4%AC%B5%AF%A4%AD%A4%EB%CD%FD%CD%B3 になります。

昔、自分がとても感銘を受けた文章なので、このまま読めなくなるのはとてももったいないと思い、バックアップとして公開しています。

@n3wtron
n3wtron / simple_mjpeg_streamer_http_server
Last active December 22, 2023 13:32
Simple Python Motion Jpeg (mjpeg server) from webcam. Using: OpenCV,BaseHTTPServer
#!/usr/bin/python
'''
Author: Igor Maculan - n3wtron@gmail.com
A Simple mjpg stream http server
'''
import cv2
import Image
import threading
from BaseHTTPServer import BaseHTTPRequestHandler,HTTPServer
from SocketServer import ThreadingMixIn
@quadrismegistus
quadrismegistus / gensim_word2vec_procrustes_align.py
Last active November 16, 2023 01:57
Code for aligning two gensim word2vec models using Procrustes matrix alignment. Code ported from HistWords <https://github.com/williamleif/histwords> by William Hamilton <wleif@stanford.edu>. [NOTE: This code is DEPRECATED for latest versions of gensim. Please see instead this updated version of the code <https://gist.github.com/zhicongchen/9e23…
def smart_procrustes_align_gensim(base_embed, other_embed, words=None):
"""Procrustes align two gensim word2vec models (to allow for comparison between same word across models).
Code ported from HistWords <https://github.com/williamleif/histwords> by William Hamilton <wleif@stanford.edu>.
(With help from William. Thank you!)
First, intersect the vocabularies (see `intersection_align_gensim` documentation).
Then do the alignment on the other_embed model.
Replace the other_embed model's syn0 and syn0norm numpy matrices with the aligned version.
Return other_embed.
@daveliepmann
daveliepmann / irises.md
Last active July 21, 2023 12:08
Implementing Sugar & James’ paper, "Finding the number of clusters in a data set: An information theoretic approach" in Clojure — Part 1

Implementing the k-means jump method: Part One

This paper:

Finding the number of clusters in a data set: An information theoretic approach

CATHERINE A. SUGAR AND GARETH M. JAMES

>Marshall School of Business, University of Southern California

@erikbern
erikbern / install-tensorflow.sh
Last active June 26, 2023 00:40
Installing TensorFlow on EC2
# Note – this is not a bash script (some of the steps require reboot)
# I named it .sh just so Github does correct syntax highlighting.
#
# This is also available as an AMI in us-east-1 (virginia): ami-cf5028a5
#
# The CUDA part is mostly based on this excellent blog post:
# http://tleyden.github.io/blog/2014/10/25/cuda-6-dot-5-on-aws-gpu-instance-running-ubuntu-14-dot-04/
# Install various packages
sudo apt-get update
@mkroman
mkroman / Network.cpp
Created November 17, 2011 01:42
Uncrustify configuration inspired by Google's C++ styleguide.
#include <sys/socket.h>
#include <sys/types.h>
#include <netinet/in.h>
#include <arpa/inet.h>
#include <netdb.h>
#include <unistd.h>
#include <string.h>
#include <iostream>
#include <stdarg.h>
#include <stdio.h>
#!/usr/bin/env python
import rospy
import mavros
from geometry_msgs.msg import PoseStamped
from mavros.msg import State
from mavros.srv import CommandBool, SetMode
# callback method for state sub
current_state = State()
@gustavohenrique
gustavohenrique / pre-sharedkey-aes.py
Created September 13, 2017 17:52
An example using Python3 and AES criptography
import sys
import base64
from Crypto.Cipher import AES
class AESCipher(object):
def __init__(self, key):
self.bs = 16
self.cipher = AES.new(key, AES.MODE_ECB)
@makochang
makochang / DrawDraggingLine.cs
Created May 12, 2015 03:27
Unity マウスでドラッグした場所に線を描画(デバッグ)
using UnityEngine;
using System.Collections;
using System.Collections.Generic;
public class DrawDraggingLine : MonoBehaviour
{
//ドラッグ中に真.
private bool isDragging = false;
//線の座標リスト.
private List<Vector3> linePoints = new List<Vector3> ();
@rezoo
rezoo / caffe.md
Last active November 4, 2021 15:28

Caffe tutorial

この文章ではCNN実装であるCaffeを用いて,特徴ベクトルの抽出やパラメータの学習を行うための方法について説明する.

Caffeでサポートされている機能

以下の作業を行いたいのであれば,Caffeを用いることが望ましい.

  • CNNを利用した画像の多クラス分類
  • CNNによる特徴ベクトルの抽出
  • CNNの転移学習
  • Stacked Auto Encoder