Yutaro Ikeda ikedaosushi

## saikyo.rst

      
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                voluntas
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              September 13, 2023 02:33
            
              
                ぼくのかんがえたさいきょうの超低遅延大規模配信
              
          
    ぼくのかんがえたさいきょうの超低遅延大規模配信


更新: 2018-08-20

作者: @voluntas

バージョン: 18.8.3

URL: https://voluntas.github.io/


## detect_marker.py
import numpy as np
import random
import math
import cv2
from PIL import Image
import sys

def detect_markers(im):
 markers = []
 # 輪郭線抽出のための二値化

## stacking_example.py
# -*- coding: utf-8 -*-
"""
Created on Mon Sep 23 23:16:44 2017

@author: Marios Michailidis

This is an example that performs stacking to improve mean squared error
This examples uses 2 bases learners (a linear regression and a random forest)
and  linear regression (again) as a meta learner to achieve the best score.
The initial train data are split in 2 halves to commence the stacking.

## understanding_webrtc_internals.rst

      
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              54 stars
            
          
                voluntas
                / understanding_webrtc_internals.rst
            
            
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              May 21, 2024 13:55
            
              
                詳解 WebRTC
              
          
    詳解 WebRTC


更新: 2017-09-26

作者: @voluntas

作者サイト: http://voluntas.github.io/

バージョン: 1.2.1

セッション日時: 2017-09-24 14:20 - 15:00

セッション場所: 5号館3F
更新:	2018-08-20
作者:	@voluntas
バージョン:	18.8.3
URL:	https://voluntas.github.io/
	import numpy as np
	import random
	import math
	import cv2
	from PIL import Image
	import sys

	def detect_markers(im):
	markers = []
	# 輪郭線抽出のための二値化
	# -- coding: utf-8 --
	"""
	Created on Mon Sep 23 23:16:44 2017

	@author: Marios Michailidis

	This is an example that performs stacking to improve mean squared error
	This examples uses 2 bases learners (a linear regression and a random forest)
	and linear regression (again) as a meta learner to achieve the best score.
	The initial train data are split in 2 halves to commence the stacking.
更新:	2017-09-26
作者:	@voluntas
作者サイト:	http://voluntas.github.io/
バージョン:	1.2.1
セッション日時:	2017-09-24 14:20 - 15:00
セッション場所:	5号館3F