dockerで機械学習(4) with anaconda(4)「仕事ではじめる機械学習」有賀康顕、中山心太、西林孝　著

# 1.すぐにプログラムを動かしたい方へ(as soon as you want)

「仕事ではじめる機械学習」有賀康顕、中山心太、西林孝　著
![picture_large978-4-87311-821-5.jpg](https://qiita-image-store.s3.amazonaws.com/0/51423/800b8b0a-0963-460b-c6d5-06df7649ad7a.jpeg)
https://www.oreilly.co.jp/books/9784873118215/

<この項は書きかけです。順次追記します。>
## docker
dockerは複数人で同じ設定で作業、実験、実習ができるとても便利な道具です。
それまで利用していた環境とは全く別に作ります。
それまでどのような設定をしていても、dockerが起動し、ネットがつながれば大丈夫です。ここでは、本で紹介しているdockerの資料に直接基づかず、すぐに利用できる環境を提供し、またご自身で新たに導入したソフトを含めて、保存しておく方法を紹介します。

### docker起動
dockerを導入し、Windows, Macではdockerを起動しておいてください。
Windowsでは、BiosでIntel Virtualizationをenableにしないとdockerが起動しない場合があります。
また、セキュリティの警告などが出ることがあります。
システム管理者での作業が必要になります。

### docker pull and run

第1章までやった段階

```
$ docker pull kaizenjapan/anaconda-ml

$ docker run -it -p 8888:8888 kaizenjapan/anaconda-ml /bin/bash
```


dockerの中と、dockerを起動したOSのシェルとが表示が似ている場合には、どちらで操作しているか間違えることがあります。dockerの入力促進記号(comman prompt)は、docker idの数字が何桁かついています。

## 第1章

以下のshell sessionでは

```
(base) root@f2854307a1c3:/#
```
は入力促進記号(comman prompt)です。実際には数字の部分が違うかもしれません。この行の#の右側を入力してください。
それ以外の行は出力です。出力にエラー、違いがあれば、コメント欄などでご連絡くださると幸いです。
それぞれの章のフォルダに移動します。

```shell-session:docker
(base) root@f2854307a1c3:/# ls
bin  boot  deep-learning-with-keras-ja	dev  etc  home	lib  lib64  media  mnt	opt  proc  root  run  sbin  srv  sys  tmp  usr	var

(base) root@f2854307a1c3:/# cd deep-learning-with-keras-ja/

(base) root@f2854307a1c3:/deep-learning-with-keras-ja# ls
README.md  ch01  ch02  ch03  ch04  ch05  ch06  ch07  ch08  deep-learning-with-keras-ja.png

(base) root@f2854307a1c3:/ml-at-work# cd chap08
(base) root@f2854307a1c3:/ml-at-work/chap08# ls
kickstarter_clawler.py	kickstarter_result_to_csv.py  kickstarter_slide.pptx

(base) root@f2854307a1c3:/ml-at-work/chap08#  jupyter notebook --ip=0.0.0.0 --allow-root
```

dockerを起動したOSのブラウザで localhost:8888 を表示してください。

### kickstarter_clawler.py

```shell-session

(base) root@f2854307a1c3:/ml-at-work/chap08# python kickstarter_clawler.py 
16 progress 12 / 35789 0.03 %
16 progress 24 / 35789 0.07 %
16 progress 36 / 35789 0.1 %
16 progress 48 / 35789 0.13 %
(中略)

```

### kickstarter_result_to_csv.py

```
(base) root@f2854307a1c3:/ml-at-work/chap08# python kickstarter_result_to_csv.py

```


# 2. dockerを自力で構築する方へ
anaconda/keras/tensorflow方針(docker deploy policy)

ここから下は、上記のpullしていただいたdockerをどういう方針で、どういう手順で作ったかを記録します。
上記のdockerを利用する上での参考資料です。１章の続きをする上では必要ありません。
自力でdocker/anaconda/keras/tensorflowを構築する場合の手順になります。
dockerfileを作る方法ではありません。ごめんなさい。

## docker
ubuntu, debianなどのLinuxを、linux, windows, mac osから共通に利用できる仕組み。
利用するOSの設定を変更せずに利用できるのがよい。
同じ仕様で、大量の人が利用することができる。

ソフトウェアの開発元が公式に対応しているものと、利用者が便利に仕立てたものの両方が利用可能である。今回は、公式に配布しているものを、自分で仕立てて、他の人にも利用できるようにする。


## python
DeepLearningの実習をPhthonで行って来た。
pythonを使う理由は、多くの機械学習の仕組みがpythonで利用できることと、Rなどの統計解析の仕組みもpythonから容易に利用できることがある。


### anaconda
pythonには、2と３という版の違いと、配布方法の違いなどがある。
Anacondaでpython3をこの１年半利用してきた。

Anacondaを利用した理由は、統計解析のライブラリと、JupyterNotebookが初めから入っているからである。

## docker公式配布
ubuntu, debianなどのOSの公式配布,gcc, anacondaなどの言語の公式配布などがある。
これらを利用し、docker-hubに登録することにより、公式配布の質の確認と、変更権を含む幅広い情報の共有ができる。dockerが公式配布するものではなく、それぞれのソフト提供者の公式配布という意味。

## docker pull
docker公式配布の利用は、URLからpullすることで実現する。

### docker Anaconda
anacondaが公式配布しているものを利用。


```
$  docker pull continuumio/anaconda3
Using default tag: latest
latest: Pulling from continuumio/anaconda3
cc1a78bfd46b: Pull complete 
314b82d3c9fe: Pull complete 
adebea299011: Pull complete 
f7baff790e81: Pull complete 
Digest: sha256:e07b9ca98ac1eeb1179dbf0e0bbcebd87701f8654878d6d8ce164d71746964d1
Status: Downloaded newer image for continuumio/anaconda3:latest
```

## docker run
実行はdocker runである。今回はブラウザで閲覧するため-pの設定を行う。

```
$ docker run -it -p 8888:8888 -p 6006:6006  continuumio/anaconda3 /bin/bash
(base) root@0da2c87a513f:/# 
```


## apt
ubuntu, debianはDebian系の道具類の配布の仕組みが利用できる。
apt-getのよいところは、debianというカーネルの開発者が大勢集まっており、基本機能の整合性を厳密に取っているところにある。
通信規約の改良にあたって、Linuxのカーネルに手を入れる必要があったときに、ソースをaptで導入し、patch適用後コンパイルしてもエラーなくコンパイルできて以来、linuxといえばaptが利用できるDebian系を愛用している。
Raspberry PIで有名なRaspbianもDebian系で、aptが利用できる。
そのため、dockerで作業した状況をRaspbianで再現することはとても容易である。

```
(base) root@0da2c87a513f:/# apt update
Ign:1 http://deb.debian.org/debian stretch InRelease
Get:2 http://security.debian.org/debian-security stretch/updates InRelease [94.3 kB]
Get:3 http://deb.debian.org/debian stretch-updates InRelease [91.0 kB]
Get:4 http://deb.debian.org/debian stretch Release [118 kB]              
Get:5 http://security.debian.org/debian-security stretch/updates/main amd64 Packages [549 kB]
Get:6 http://deb.debian.org/debian stretch-updates/main amd64 Packages.diff/Index [5164 B]
Get:7 http://deb.debian.org/debian stretch Release.gpg [2434 B]
Get:8 http://deb.debian.org/debian stretch-updates/main amd64 Packages 2018-07-20-2027.50.pdiff [1134 B]
Get:9 http://deb.debian.org/debian stretch-updates/main amd64 Packages 2018-07-31-2010.17.pdiff [1388 B]
Get:9 http://deb.debian.org/debian stretch-updates/main amd64 Packages 2018-07-31-2010.17.pdiff [1388 B]
Get:10 http://deb.debian.org/debian stretch/main amd64 Packages [9500 kB]
Fetched 10.4 MB in 27s (380 kB/s)                                                                                                                    
Reading package lists... Done
```

## conda install

pythonには、pipというpythonのライブラリ等を導入する仕組みがある。
今回は、condaというanaconda専用のライブラリ導入の仕組みを利用してみる。
pipではなくcondaを利用した理由は、本に書いてあるように作業したらエラーでうまくいかなかったことがあったから。
condaによるanacondaの更新は下記。

```
# conda update --prefix /opt/conda anaconda
```

## git
本にあるURLからgitで取得する。

```
(base) root@b789c278e622:/# git clone https://github.com/oreilly-japan/ml-at-work.git
```
## pip install

```
(base) root@f2854307a1c3:/ml-at-work/chap08# pip install --upgrade pip
Collecting pip
  Downloading https://files.pythonhosted.org/packages/5f/25/e52d3f31441505a5f3af41213346e5b6c221c9e086a166f3703d2ddaf940/pip-18.0-py2.py3-none-any.whl (1.3MB)
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distributed 1.21.8 requires msgpack, which is not installed.
Installing collected packages: pip
  Found existing installation: pip 10.0.1
    Uninstalling pip-10.0.1:
      Successfully uninstalled pip-10.0.1
Successfully installed pip-18.0

(base) root@f2854307a1c3:/ml-at-work# pip install -r requirements.txt -c constraints.txt
Collecting Cython==0.27.3 (from -c constraints.txt (line 3))
  Downloading https://files.pythonhosted.org/packages/e9/91/46cb3f4c73f1e96faa517f96e9d12de5b8c97d404c7ab71553da0e58c980/Cython-0.27.3-cp36-cp36m-manylinux1_x86_64.whl (3.1MB)
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Collecting python-dateutil==2.6.1 (from -c constraints.txt (line 36))
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Collecting pytz==2017.2 (from -c constraints.txt (line 37))
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Collecting scipy==1.0.0 (from -c constraints.txt (line 41))
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Collecting terminado==0.6 (from -c constraints.txt (line 45))
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Collecting ipython==6.2.1 (from -c constraints.txt (line 9))
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Collecting widgetsnbextension==3.0.3 (from -c constraints.txt (line 51))
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Requirement already satisfied: pickleshare==0.7.4 in /opt/conda/lib/python3.6/site-packages (from -c constraints.txt (line 31)) (0.7.4)
Requirement already satisfied: MarkupSafe==1.0 in /opt/conda/lib/python3.6/site-packages (from -c constraints.txt (line 19)) (1.0)
Collecting html5lib==1.0b10 (from -c constraints.txt (line 7))
  Downloading https://files.pythonhosted.org/packages/2f/74/7793ca2d36f676b740efc04b7ba887c610119beb5841d1805cb3515616cb/html5lib-1.0b10-py2.py3-none-any.whl (112kB)
    100% |████████████████████████████████| 112kB 619kB/s 
Collecting parso==0.1.0 (from -c constraints.txt (line 28))
  Downloading https://files.pythonhosted.org/packages/fc/af/32b2a0d8b2e5d5c82f6f29752a243255beb4b281617e9581b3d7784aec7a/parso-0.1.0-py2.py3-none-any.whl (89kB)
    100% |████████████████████████████████| 92kB 586kB/s 
Requirement already satisfied: webencodings==0.5.1 in /opt/conda/lib/python3.6/site-packages (from -c constraints.txt (line 50)) (0.5.1)
Building wheels for collected packages: terminado, tornado
  Running setup.py bdist_wheel for terminado ... done
  Stored in directory: /root/.cache/pip/wheels/5b/04/58/844654a8da3bb183aa18673c959eedbb992314261049d7e5e1
  Running setup.py bdist_wheel for tornado ... done
  Stored in directory: /root/.cache/pip/wheels/a5/59/09/79aad6522a5811b546e94d55c1535702dcad35880a09b03471
Successfully built terminado tornado
distributed 1.21.8 requires msgpack, which is not installed.
flask 1.0.2 has requirement Jinja2>=2.10, but you'll have jinja2 2.9.6 which is incompatible.
Installing collected packages: html5lib, bleach, Cython, decorator, numpy, scipy, fastFM, pexpect, parso, jedi, ipython, pyzmq, jupyter-core, python-dateutil, jupyter-client, tornado, ipykernel, Jinja2, terminado, notebook, widgetsnbextension, ipywidgets, pytz, matplotlib, mistune, pandas, patsy, statsmodels
  Found existing installation: html5lib 1.0.1
    Uninstalling html5lib-1.0.1:
      Successfully uninstalled html5lib-1.0.1
  Found existing installation: bleach 2.1.3
    Uninstalling bleach-2.1.3:
      Successfully uninstalled bleach-2.1.3
  Found existing installation: Cython 0.28.2
    Uninstalling Cython-0.28.2:
      Successfully uninstalled Cython-0.28.2
  Found existing installation: decorator 4.3.0
    Uninstalling decorator-4.3.0:
      Successfully uninstalled decorator-4.3.0
  Found existing installation: numpy 1.14.3
    Uninstalling numpy-1.14.3:
      Successfully uninstalled numpy-1.14.3
  Found existing installation: scipy 1.1.0
    Uninstalling scipy-1.1.0:
      Successfully uninstalled scipy-1.1.0
  Found existing installation: pexpect 4.5.0
    Uninstalling pexpect-4.5.0:
      Successfully uninstalled pexpect-4.5.0
  Found existing installation: parso 0.2.0
    Uninstalling parso-0.2.0:
      Successfully uninstalled parso-0.2.0
  Found existing installation: jedi 0.12.0
    Uninstalling jedi-0.12.0:
      Successfully uninstalled jedi-0.12.0
  Found existing installation: ipython 6.4.0
    Uninstalling ipython-6.4.0:
      Successfully uninstalled ipython-6.4.0
  Found existing installation: pyzmq 17.0.0
    Uninstalling pyzmq-17.0.0:
      Successfully uninstalled pyzmq-17.0.0
  Found existing installation: jupyter-core 4.4.0
    Uninstalling jupyter-core-4.4.0:
      Successfully uninstalled jupyter-core-4.4.0
  Found existing installation: python-dateutil 2.7.3
    Uninstalling python-dateutil-2.7.3:
      Successfully uninstalled python-dateutil-2.7.3
  Found existing installation: jupyter-client 5.2.3
    Uninstalling jupyter-client-5.2.3:
      Successfully uninstalled jupyter-client-5.2.3
  Found existing installation: tornado 5.0.2
    Uninstalling tornado-5.0.2:
      Successfully uninstalled tornado-5.0.2
  Found existing installation: ipykernel 4.8.2
    Uninstalling ipykernel-4.8.2:
      Successfully uninstalled ipykernel-4.8.2
  Found existing installation: Jinja2 2.10
    Uninstalling Jinja2-2.10:
      Successfully uninstalled Jinja2-2.10
  Found existing installation: terminado 0.8.1
Cannot uninstall 'terminado'. It is a distutils installed project and thus we cannot accurately determine which files belong to it which would lead to only a partial uninstall.
```

# 3. docker hub 登録
ここからは、新たにソフトを導入したdockerを自分のhubに登録する方法です。
ご自身で何かソフトウェアを導入されたら、ぜひhubに登録することをお勧めします。

docker hubへのID登録が必要になります。

続きの作業を誰かに依頼したり、エラーがでてわからなくなったときに、対処方法を問い合わせるのにも役立ちます。
kaizenjapanは小川清のIDです。ご自身のIDで読み替えて、ご登録ください。
## docker push

```
$ docker ps 
CONTAINER ID        IMAGE                   COMMAND                  CREATED             STATUS              PORTS                                            NAMES
b789c278e622        continuumio/anaconda3   "/usr/bin/tini -- /b…"   21 hours ago        Up 21 hours         0.0.0.0:8888->8888/tcp   peaceful_noice

$ docker commit b789c278e622 kaizenjapan/anaconda-ml

$ docker push kaizenjapan/anaconda-ml:latest
The push refers to repository [docker.io/kaizenjapan/anaconda-keras]
95456260f6d1: Pushed 
6410333f34cf: Pushed 
cf342e34eca3: Pushing [==================>                                ]   1.21GB/3.27GB
cea95006e36a: Pushed 
0f3a12fef684: Pushed 

```


# 4. 参考資料(reference)

機械学習を半自動化するauto-sklearnの環境構築（Mac&Docker）
https://qiita.com/inoue0426/items/ffd7f4235dcfde88942b

dockerで機械学習(1) with anaconda(1)「ゼロから作るDeep Learning - Pythonで学ぶディープラーニングの理論と実装」斎藤 康毅 著
https://qiita.com/kaizen_nagoya/items/a7e94ef6dca128d035ab


dockerで機械学習(2)with anaconda(2)「ゼロから作るDeep Learning2自然言語処理編」斎藤 康毅 著
https://qiita.com/kaizen_nagoya/items/3b80dfc76933cea522c6

dockerで機械学習(3)with anaconda(3)「直感Deep Learning」Antonio Gulli、Sujit Pal　第1章,第2章
https://qiita.com/kaizen_nagoya/items/483ae708c71c88419c32

dockerで機械学習(71) 環境構築(1) docker どっかーら、どーやってもエラーばっかり。
https://qiita.com/kaizen_nagoya/items/690d806a4760d9b9e040
dockerで機械学習(72) 環境構築(2) Docker for Windows
https://qiita.com/kaizen_nagoya/items/c4daa5cf52e9f0c2c002
dockerで機械学習(73) 環境構築(3) docker/linux/macos bash スクリプト, ms-dos batchファイル
https://qiita.com/kaizen_nagoya/items/3f7b39110b7f303a5558
dockerで機械学習(74) 環境構築(4) R 難関いくつ？
https://qiita.com/kaizen_nagoya/items/5fb44773bc38574bcf1c
dockerで機械学習(75)環境構築(5)docker関連ファイルの管理
https://qiita.com/kaizen_nagoya/items/4f03df9a42c923087b5d
「名古屋のIoTは名古屋のOSで」Dockerをどっかーらどうやって使えばいいんでしょう。TOPPERS/FMP on RaspberryPi with Macintosh編 ５つの関門
https://qiita.com/kaizen_nagoya/items/9c46c6da8ceb64d2d7af

64bitCPUへの道 and/or 64歳の決意
https://qiita.com/kaizen_nagoya/items/cfb5ffa24ded23ab3f60

ゼロから作るDeepLearning２自然言語処理編　読書会の進め方（例）
https://qiita.com/kaizen_nagoya/items/025eb3f701b36209302e

## Docker for windows
BIOSでInte Virtualization をenableにしていないと動作しないことを書いていない記事が多い。なぜかは調査中。

今更Docker for Windowsをインストールしたのでその備忘録
https://qiita.com/toro_ponz/items/d66d5571c4646ad33279

Docker for Windowsで起動時に「Docker for Windows - Access denied」と表示される場合の対処法
https://qiita.com/toro_ponz/items/d75706a3039f00ba1205

Windows 10 Hyper-Vユーザに送るDockerの解説
https://qiita.com/banban525/items/48aec05671c3c77d454a

Docker for WindowsでDockerを学ぶ (バージョンCE 17.06.2)
https://qiita.com/rubytomato@github/items/eec2118e89ee9bd8d17a

Docker for Windows をインストールする
https://qiita.com/centipede/items/f8d77b66343ef5096eee

Windows 10(Surface)にDocker for Windowsをインストール
https://qiita.com/chakimar/items/868298096ebf9186d690


# 後日談

```
# pip install --upgrade pip
Collecting pip
  Downloading https://files.pythonhosted.org/packages/c2/d7/90f34cb0d83a6c5631cf71dfe64cc1054598c843a92b400e55675cc2ac37/pip-18.1-py2.py3-none-any.whl (1.3MB)
    100% |████████████████████████████████| 1.3MB 1.7MB/s 
twisted 18.7.0 requires PyHamcrest>=1.9.0, which is not installed.
Installing collected packages: pip
  Found existing installation: pip 10.0.1
    Uninstalling pip-10.0.1:
      Successfully uninstalled pip-10.0.1
Successfully installed pip-18.1
```

```
/# cd ml-at-work/
(base) root@1f725dc9d543:/ml-at-work# pip install -r requirements.txt -c constraints.txt
Collecting Cython==0.27.3 (from -c constraints.txt (line 3))
  Downloading https://files.pythonhosted.org/packages/ee/2a/c4d2cdd19c84c32d978d18e9355d1ba9982a383de87d0fcb5928553d37f4/Cython-0.27.3.tar.gz (1.8MB)
    100% |████████████████████████████████| 1.8MB 1.8MB/s 
Collecting fastFM==0.2.11 (from -c constraints.txt (line 6))
  Could not find a version that satisfies the requirement fastFM==0.2.11 (from -c constraints.txt (line 6)) (from versions: 0.2.3, 0.2.5, 0.2.6, 0.2.9, 0.2.10)
No matching distribution found for fastFM==0.2.11 (from -c constraints.txt (line 6))
(base) root@1f725dc9d543:/ml-at-work# pip install -r requirements.txt
Requirement already satisfied: matplotlib in /opt/conda/lib/python3.7/site-packages (from -r requirements.txt (line 1)) (2.2.3)
Requirement already satisfied: Cython in /opt/conda/lib/python3.7/site-packages (from -r requirements.txt (line 2)) (0.28.5)
Requirement already satisfied: numpy in /opt/conda/lib/python3.7/site-packages (from -r requirements.txt (line 3)) (1.15.1)
Requirement already satisfied: scikit-learn in /opt/conda/lib/python3.7/site-packages (from -r requirements.txt (line 4)) (0.19.2)
Collecting fastFM (from -r requirements.txt (line 5))
  Downloading https://files.pythonhosted.org/packages/41/31/7fb81ab6b11bd35f085eb9b4d0e2e48158056e4a639a1e67057519259512/fastFM-0.2.10.tar.gz (1.6MB)
    100% |████████████████████████████████| 1.6MB 755kB/s 
Requirement already satisfied: jupyter in /opt/conda/lib/python3.7/site-packages (from -r requirements.txt (line 6)) (1.0.0)
Requirement already satisfied: notebook in /opt/conda/lib/python3.7/site-packages (from -r requirements.txt (line 7)) (5.6.0)
Requirement already satisfied: pandas in /opt/conda/lib/python3.7/site-packages (from -r requirements.txt (line 8)) (0.23.4)
Requirement already satisfied: statsmodels in /opt/conda/lib/python3.7/site-packages (from -r requirements.txt (line 9)) (0.9.0)
Requirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.7/site-packages (from matplotlib->-r requirements.txt (line 1)) (0.10.0)
Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /opt/conda/lib/python3.7/site-packages (from matplotlib->-r requirements.txt (line 1)) (2.2.0)
Requirement already satisfied: python-dateutil>=2.1 in /opt/conda/lib/python3.7/site-packages (from matplotlib->-r requirements.txt (line 1)) (2.7.3)
Requirement already satisfied: pytz in /opt/conda/lib/python3.7/site-packages (from matplotlib->-r requirements.txt (line 1)) (2018.5)
Requirement already satisfied: six>=1.10 in /opt/conda/lib/python3.7/site-packages (from matplotlib->-r requirements.txt (line 1)) (1.11.0)
Requirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/lib/python3.7/site-packages (from matplotlib->-r requirements.txt (line 1)) (1.0.1)
Requirement already satisfied: scipy in /opt/conda/lib/python3.7/site-packages (from fastFM->-r requirements.txt (line 5)) (1.1.0)
Requirement already satisfied: nbconvert in /opt/conda/lib/python3.7/site-packages (from jupyter->-r requirements.txt (line 6)) (5.4.0)
Requirement already satisfied: ipykernel in /opt/conda/lib/python3.7/site-packages (from jupyter->-r requirements.txt (line 6)) (4.9.0)
Requirement already satisfied: jupyter-console in /opt/conda/lib/python3.7/site-packages (from jupyter->-r requirements.txt (line 6)) (5.2.0)
Requirement already satisfied: ipywidgets in /opt/conda/lib/python3.7/site-packages (from jupyter->-r requirements.txt (line 6)) (7.4.1)
Requirement already satisfied: qtconsole in /opt/conda/lib/python3.7/site-packages (from jupyter->-r requirements.txt (line 6)) (4.4.1)
Requirement already satisfied: jupyter-core>=4.4.0 in /opt/conda/lib/python3.7/site-packages (from notebook->-r requirements.txt (line 7)) (4.4.0)
Requirement already satisfied: Send2Trash in /opt/conda/lib/python3.7/site-packages (from notebook->-r requirements.txt (line 7)) (1.5.0)
Requirement already satisfied: jupyter-client>=5.2.0 in /opt/conda/lib/python3.7/site-packages (from notebook->-r requirements.txt (line 7)) (5.2.3)
Requirement already satisfied: prometheus-client in /opt/conda/lib/python3.7/site-packages (from notebook->-r requirements.txt (line 7)) (0.3.1)
Requirement already satisfied: jinja2 in /opt/conda/lib/python3.7/site-packages (from notebook->-r requirements.txt (line 7)) (2.10)
Requirement already satisfied: terminado>=0.8.1 in /opt/conda/lib/python3.7/site-packages (from notebook->-r requirements.txt (line 7)) (0.8.1)
Requirement already satisfied: tornado>=4 in /opt/conda/lib/python3.7/site-packages (from notebook->-r requirements.txt (line 7)) (5.1)
Requirement already satisfied: pyzmq>=17 in /opt/conda/lib/python3.7/site-packages (from notebook->-r requirements.txt (line 7)) (17.1.2)
Requirement already satisfied: nbformat in /opt/conda/lib/python3.7/site-packages (from notebook->-r requirements.txt (line 7)) (4.4.0)
Requirement already satisfied: traitlets>=4.2.1 in /opt/conda/lib/python3.7/site-packages (from notebook->-r requirements.txt (line 7)) (4.3.2)
Requirement already satisfied: ipython-genutils in /opt/conda/lib/python3.7/site-packages (from notebook->-r requirements.txt (line 7)) (0.2.0)
Requirement already satisfied: setuptools in /opt/conda/lib/python3.7/site-packages (from kiwisolver>=1.0.1->matplotlib->-r requirements.txt (line 1)) (40.2.0)
Requirement already satisfied: mistune>=0.8.1 in /opt/conda/lib/python3.7/site-packages (from nbconvert->jupyter->-r requirements.txt (line 6)) (0.8.3)
Requirement already satisfied: pygments in /opt/conda/lib/python3.7/site-packages (from nbconvert->jupyter->-r requirements.txt (line 6)) (2.2.0)
Requirement already satisfied: entrypoints>=0.2.2 in /opt/conda/lib/python3.7/site-packages (from nbconvert->jupyter->-r requirements.txt (line 6)) (0.2.3)
Requirement already satisfied: bleach in /opt/conda/lib/python3.7/site-packages (from nbconvert->jupyter->-r requirements.txt (line 6)) (2.1.4)
Requirement already satisfied: pandocfilters>=1.4.1 in /opt/conda/lib/python3.7/site-packages (from nbconvert->jupyter->-r requirements.txt (line 6)) (1.4.2)
Requirement already satisfied: testpath in /opt/conda/lib/python3.7/site-packages (from nbconvert->jupyter->-r requirements.txt (line 6)) (0.3.1)
Requirement already satisfied: defusedxml in /opt/conda/lib/python3.7/site-packages (from nbconvert->jupyter->-r requirements.txt (line 6)) (0.5.0)
Requirement already satisfied: ipython>=4.0.0 in /opt/conda/lib/python3.7/site-packages (from ipykernel->jupyter->-r requirements.txt (line 6)) (6.5.0)
Requirement already satisfied: prompt-toolkit<2.0.0,>=1.0.0 in /opt/conda/lib/python3.7/site-packages (from jupyter-console->jupyter->-r requirements.txt (line 6)) (1.0.15)
Requirement already satisfied: widgetsnbextension~=3.4.0 in /opt/conda/lib/python3.7/site-packages (from ipywidgets->jupyter->-r requirements.txt (line 6)) (3.4.1)
Requirement already satisfied: MarkupSafe>=0.23 in /opt/conda/lib/python3.7/site-packages (from jinja2->notebook->-r requirements.txt (line 7)) (1.0)
Requirement already satisfied: jsonschema!=2.5.0,>=2.4 in /opt/conda/lib/python3.7/site-packages (from nbformat->notebook->-r requirements.txt (line 7)) (2.6.0)
Requirement already satisfied: decorator in /opt/conda/lib/python3.7/site-packages (from traitlets>=4.2.1->notebook->-r requirements.txt (line 7)) (4.3.0)
Requirement already satisfied: html5lib!=1.0b1,!=1.0b2,!=1.0b3,!=1.0b4,!=1.0b5,!=1.0b6,!=1.0b7,!=1.0b8,>=0.99999999pre in /opt/conda/lib/python3.7/site-packages (from bleach->nbconvert->jupyter->-r requirements.txt (line 6)) (1.0.1)
Requirement already satisfied: simplegeneric>0.8 in /opt/conda/lib/python3.7/site-packages (from ipython>=4.0.0->ipykernel->jupyter->-r requirements.txt (line 6)) (0.8.1)
Requirement already satisfied: jedi>=0.10 in /opt/conda/lib/python3.7/site-packages (from ipython>=4.0.0->ipykernel->jupyter->-r requirements.txt (line 6)) (0.12.1)
Requirement already satisfied: pickleshare in /opt/conda/lib/python3.7/site-packages (from ipython>=4.0.0->ipykernel->jupyter->-r requirements.txt (line 6)) (0.7.4)
Requirement already satisfied: backcall in /opt/conda/lib/python3.7/site-packages (from ipython>=4.0.0->ipykernel->jupyter->-r requirements.txt (line 6)) (0.1.0)
Requirement already satisfied: pexpect; sys_platform != "win32" in /opt/conda/lib/python3.7/site-packages (from ipython>=4.0.0->ipykernel->jupyter->-r requirements.txt (line 6)) (4.6.0)
Requirement already satisfied: wcwidth in /opt/conda/lib/python3.7/site-packages (from prompt-toolkit<2.0.0,>=1.0.0->jupyter-console->jupyter->-r requirements.txt (line 6)) (0.1.7)
Requirement already satisfied: webencodings in /opt/conda/lib/python3.7/site-packages (from html5lib!=1.0b1,!=1.0b2,!=1.0b3,!=1.0b4,!=1.0b5,!=1.0b6,!=1.0b7,!=1.0b8,>=0.99999999pre->bleach->nbconvert->jupyter->-r requirements.txt (line 6)) (0.5.1)
Requirement already satisfied: parso>=0.3.0 in /opt/conda/lib/python3.7/site-packages (from jedi>=0.10->ipython>=4.0.0->ipykernel->jupyter->-r requirements.txt (line 6)) (0.3.1)
Requirement already satisfied: ptyprocess>=0.5 in /opt/conda/lib/python3.7/site-packages (from pexpect; sys_platform != "win32"->ipython>=4.0.0->ipykernel->jupyter->-r requirements.txt (line 6)) (0.6.0)
Building wheels for collected packages: fastFM
  Running setup.py bdist_wheel for fastFM ... error
  Complete output from command /opt/conda/bin/python -u -c "import setuptools, tokenize;__file__='/tmp/pip-install-l8x_0ss5/fastFM/setup.py';f=getattr(tokenize, 'open', open)(__file__);code=f.read().replace('\r\n', '\n');f.close();exec(compile(code, __file__, 'exec'))" bdist_wheel -d /tmp/pip-wheel-jtkzcf0u --python-tag cp37:
  running bdist_wheel
  running build
  running build_py
  creating build
  creating build/lib.linux-x86_64-3.7
  creating build/lib.linux-x86_64-3.7/fastFM
  copying fastFM/als.py -> build/lib.linux-x86_64-3.7/fastFM
  copying fastFM/base.py -> build/lib.linux-x86_64-3.7/fastFM
  copying fastFM/utils.py -> build/lib.linux-x86_64-3.7/fastFM
  copying fastFM/validation.py -> build/lib.linux-x86_64-3.7/fastFM
  copying fastFM/mcmc.py -> build/lib.linux-x86_64-3.7/fastFM
  copying fastFM/sgd.py -> build/lib.linux-x86_64-3.7/fastFM
  copying fastFM/__init__.py -> build/lib.linux-x86_64-3.7/fastFM
  copying fastFM/bpr.py -> build/lib.linux-x86_64-3.7/fastFM
  copying fastFM/datasets.py -> build/lib.linux-x86_64-3.7/fastFM
  running build_ext
  skipping 'fastFM/ffm.c' Cython extension (up-to-date)
  building 'ffm' extension
  creating build/temp.linux-x86_64-3.7
  creating build/temp.linux-x86_64-3.7/fastFM
  gcc -pthread -B /opt/conda/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -IfastFM/ -IfastFM-core/include/ -IfastFM-core/externals/CXSparse/Include/ -I/opt/conda/lib/python3.7/site-packages/numpy/core/include -I/opt/conda/include/python3.7m -c fastFM/ffm.c -o build/temp.linux-x86_64-3.7/fastFM/ffm.o
  unable to execute 'gcc': No such file or directory
  error: command 'gcc' failed with exit status 1
  
  ----------------------------------------
  Failed building wheel for fastFM
  Running setup.py clean for fastFM
Failed to build fastFM
Installing collected packages: fastFM
  Running setup.py install for fastFM ... error
    Complete output from command /opt/conda/bin/python -u -c "import setuptools, tokenize;__file__='/tmp/pip-install-l8x_0ss5/fastFM/setup.py';f=getattr(tokenize, 'open', open)(__file__);code=f.read().replace('\r\n', '\n');f.close();exec(compile(code, __file__, 'exec'))" install --record /tmp/pip-record-g7xwdofi/install-record.txt --single-version-externally-managed --compile:
    running install
    running build
    running build_py
    creating build
    creating build/lib.linux-x86_64-3.7
    creating build/lib.linux-x86_64-3.7/fastFM
    copying fastFM/als.py -> build/lib.linux-x86_64-3.7/fastFM
    copying fastFM/base.py -> build/lib.linux-x86_64-3.7/fastFM
    copying fastFM/utils.py -> build/lib.linux-x86_64-3.7/fastFM
    copying fastFM/validation.py -> build/lib.linux-x86_64-3.7/fastFM
    copying fastFM/mcmc.py -> build/lib.linux-x86_64-3.7/fastFM
    copying fastFM/sgd.py -> build/lib.linux-x86_64-3.7/fastFM
    copying fastFM/__init__.py -> build/lib.linux-x86_64-3.7/fastFM
    copying fastFM/bpr.py -> build/lib.linux-x86_64-3.7/fastFM
    copying fastFM/datasets.py -> build/lib.linux-x86_64-3.7/fastFM
    running build_ext
    skipping 'fastFM/ffm.c' Cython extension (up-to-date)
    building 'ffm' extension
    creating build/temp.linux-x86_64-3.7
    creating build/temp.linux-x86_64-3.7/fastFM
    gcc -pthread -B /opt/conda/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -IfastFM/ -IfastFM-core/include/ -IfastFM-core/externals/CXSparse/Include/ -I/opt/conda/lib/python3.7/site-packages/numpy/core/include -I/opt/conda/include/python3.7m -c fastFM/ffm.c -o build/temp.linux-x86_64-3.7/fastFM/ffm.o
    unable to execute 'gcc': No such file or directory
    error: command 'gcc' failed with exit status 1
    
    ----------------------------------------
Command "/opt/conda/bin/python -u -c "import setuptools, tokenize;__file__='/tmp/pip-install-l8x_0ss5/fastFM/setup.py';f=getattr(tokenize, 'open', open)(__file__);code=f.read().replace('\r\n', '\n');f.close();exec(compile(code, __file__, 'exec'))" install --record /tmp/pip-record-g7xwdofi/install-record.txt --single-version-externally-managed --compile" failed with error code 1 in /tmp/pip-install-l8x_0ss5/fastFM/
```


<この項は書きかけです。順次追記します。>
This article is not completed. I will add some words and/or centences in order.

# Qiita Calendar 2024
2024 参加・主催Calendarと投稿記事一覧 Qiita(248)
https://qiita.com/kaizen_nagoya/items/d80b8fbac2496df7827f

主催Calendar2024分析 Qiita(254)
https://qiita.com/kaizen_nagoya/items/15807336d583076f70bc

Calendar 統計
https://qiita.com/kaizen_nagoya/items/e315558dcea8ee3fe43e

LLM 関連 Calendar 2024 
https://qiita.com/kaizen_nagoya/items/c36033cf66862d5496fa

Large Language Model Related Calendar 
https://qiita.com/kaizen_nagoya/items/3beb0bc3fb71e3ae6d66

博士論文 Calendar 2024 を開催します。 
https://qiita.com/kaizen_nagoya/items/51601357efbcaf1057d0

博士論文(0)関連記事一覧
https://qiita.com/kaizen_nagoya/items/8f223a760e607b705e78

# 自己記事一覧
Qiitaで逆リンクを表示しなくなったような気がする。時々、スマフォで表示するとあらわっることがあり、完全に削除したのではなさそう。

４月以降、せっせとリンクリストを作り、統計を取って確率を説明しようとしている。
2025年２月末を目標にしている。

一覧の一覧( The directory of directories of mine.) Qiita(100)
https://qiita.com/kaizen_nagoya/items/7eb0e006543886138f39

仮説（0）一覧（目標100現在40）
https://qiita.com/kaizen_nagoya/items/f000506fe1837b3590df

Qiita(0)Qiita関連記事一覧（自分）
https://qiita.com/kaizen_nagoya/items/58db5fbf036b28e9dfa6

Error一覧 error(0)
https://qiita.com/kaizen_nagoya/items/48b6cbc8d68eae2c42b8

C++ Support(0)　
https://qiita.com/kaizen_nagoya/items/8720d26f762369a80514

Coding(0) Rules, C, Secure, MISRA and so on
https://qiita.com/kaizen_nagoya/items/400725644a8a0e90fbb0

Ethernet 記事一覧　Ethernet(0)
https://qiita.com/kaizen_nagoya/items/88d35e99f74aefc98794

Wireshark 一覧 wireshark(0)、Ethernet(48) 
https://qiita.com/kaizen_nagoya/items/fbed841f61875c4731d0

線網（Wi-Fi）空中線(antenna)(0) 記事一覧(118/300目標)
https://qiita.com/kaizen_nagoya/items/5e5464ac2b24bd4cd001

なぜdockerで機械学習するか 書籍・ソース一覧作成中 (目標100)
https://qiita.com/kaizen_nagoya/items/ddd12477544bf5ba85e2

プログラムちょい替え（0）一覧:4件
https://qiita.com/kaizen_nagoya/items/296d87ef4bfd516bc394

言語処理100本ノックをdockerで。python覚えるのに最適。:10+12
https://qiita.com/kaizen_nagoya/items/7e7eb7c543e0c18438c4

Python(0)記事をまとめたい。
https://qiita.com/kaizen_nagoya/items/088c57d70ab6904ebb53

安全（0）安全工学シンポジウムに向けて: 21
https://qiita.com/kaizen_nagoya/items/c5d78f3def8195cb2409

プログラマによる、プログラマのための、統計(0)と確率のプログラミングとその後
https://qiita.com/kaizen_nagoya/items/6e9897eb641268766909

転職(0)一覧
https://qiita.com/kaizen_nagoya/items/f77520d378d33451d6fe

技術士(0)一覧
https://qiita.com/kaizen_nagoya/items/ce4ccf4eb9c5600b89ea

Reserchmap(0) 一覧
https://qiita.com/kaizen_nagoya/items/506c79e562f406c4257e

物理記事　上位100
https://qiita.com/kaizen_nagoya/items/66e90fe31fbe3facc6ff

量子(0) 計算機, 量子力学 
https://qiita.com/kaizen_nagoya/items/1cd954cb0eed92879fd4

数学関連記事１００
https://qiita.com/kaizen_nagoya/items/d8dadb49a6397e854c6d

coq(0) 一覧
https://qiita.com/kaizen_nagoya/items/d22f9995cf2173bc3b13

統計(0)一覧
https://qiita.com/kaizen_nagoya/items/80d3b221807e53e88aba

図(0) state, sequence and timing. UML and お絵描き 
https://qiita.com/kaizen_nagoya/items/60440a882146aeee9e8f

色(0) 記事100書く切り口 
https://qiita.com/kaizen_nagoya/items/22331c0335ed34326b9b

品質一覧 
https://qiita.com/kaizen_nagoya/items/2b99b8e9db6d94b2e971

言語・文学記事　１００
https://qiita.com/kaizen_nagoya/items/42d58d5ef7fb53c407d6

医工連携関連記事一覧
https://qiita.com/kaizen_nagoya/items/6ab51c12ba51bc260a82

水の資料集(0)　方針と成果
https://qiita.com/kaizen_nagoya/items/f5dbb30087ea732b52aa

自動車　記事　１００
https://qiita.com/kaizen_nagoya/items/f7f0b9ab36569ad409c5

通信記事１００
https://qiita.com/kaizen_nagoya/items/1d67de5e1cd207b05ef7

日本語（０）一欄
https://qiita.com/kaizen_nagoya/items/7498dcfa3a9ba7fd1e68

英語(0) 一覧
https://qiita.com/kaizen_nagoya/items/680e3f5cbf9430486c7d

音楽　一覧(0) 
https://qiita.com/kaizen_nagoya/items/b6e5f42bbfe3bbe40f5d

「@kazuo_reve 新人の方によく展開している有益な情報」確認一覧 
https://qiita.com/kaizen_nagoya/items/b9380888d1e5a042646b

鉄道（０）鉄道のシステム考察はてっちゃんがてつだってくれる
https://qiita.com/kaizen_nagoya/items/faa4ea03d91d901a618a

OSEK OS設計の基礎　OSEK(100)
https://qiita.com/kaizen_nagoya/items/7528a22a14242d2d58a3

coding (101) 一覧を作成し始めた。omake:最近のQiitaで表示しない5つの事象
https://qiita.com/kaizen_nagoya/items/20667f09f19598aedb68

官公庁・学校・公的団体（NPOを含む）システムの課題、官（０）
https://qiita.com/kaizen_nagoya/items/04ee6eaf7ec13d3af4c3

「はじめての」シリーズ　 ベクタージャパン　
https://qiita.com/kaizen_nagoya/items/2e41634f6e21a3cf74eb

AUTOSAR(0)Qiita記事一覧, OSEK(75)
https://qiita.com/kaizen_nagoya/items/89c07961b59a8754c869

プログラマが知っていると良い「公序良俗」
https://qiita.com/kaizen_nagoya/items/9fe7c0dfac2fbd77a945

LaTeX(0) 一覧　
https://qiita.com/kaizen_nagoya/items/e3f7dafacab58c499792

自動制御、制御工学一覧（０）
https://qiita.com/kaizen_nagoya/items/7767a4e19a6ae1479e6b

Rust(0) 一覧　
https://qiita.com/kaizen_nagoya/items/5e8bb080ba6ca0281927

# 関連資料
' @kazuo_reve 私が効果を確認した「小川メソッド」
https://qiita.com/kazuo_reve/items/a3ea1d9171deeccc04da

' @kazuo_reve 新人の方によく展開している有益な情報
https://qiita.com/kazuo_reve/items/d1a3f0ee48e24bba38f1

' @kazuo_reve Vモデルについて勘違いしていたと思ったこと
https://qiita.com/kazuo_reve/items/46fddb094563bd9b2e1e


## Engineering Festa 2024前に必読記事一覧
programの本質は計画だ。programは設計だ。
https://qiita.com/kaizen_nagoya/items/c8545a769c246a458c27

登壇直後版 色使い(JIS安全色) Qiita Engineer Festa 2023〜私しか得しないニッチな技術でLT〜 スライド編 0.15
https://qiita.com/kaizen_nagoya/items/f0d3070d839f4f735b2b

プログラマが知っていると良い「公序良俗」
https://qiita.com/kaizen_nagoya/items/9fe7c0dfac2fbd77a945

逆も真：社会人が最初に確かめるとよいこと。OSEK(69)、Ethernet(59)
https://qiita.com/kaizen_nagoya/items/39afe4a728a31b903ddc

統計の嘘。仮説（127）
https://qiita.com/kaizen_nagoya/items/63b48ecf258a3471c51b

自分の言葉だけで論理展開できるのが天才なら、文章の引用だけで論理展開できるのが秀才だ。仮説（136）
https://qiita.com/kaizen_nagoya/items/97cf07b9e24f860624dd

参考文献駆動執筆(references driven writing)・デンソークリエイト編
https://qiita.com/kaizen_nagoya/items/b27b3f58b8bf265a5cd1

「何を」よりも「誰を」。１０年後のために今見習いたい人たち
https://qiita.com/kaizen_nagoya/items/8045978b16eb49d572b2

Qiitaの記事に３段階または５段階で到達するための方法
https://qiita.com/kaizen_nagoya/items/6e9298296852325adc5e

出力(output)と呼ばないで。これは状態(state)です。
https://qiita.com/kaizen_nagoya/items/80b8b5913b2748867840

coding (101) 一覧を作成し始めた。omake:最近のQiitaで表示しない5つの事象
https://qiita.com/kaizen_nagoya/items/20667f09f19598aedb68

あなたは「勘違いまとめ」から、勘違いだと言っていることが勘違いだといくつ見つけられますか。人間の間違い(human error(125))の種類と対策
https://qiita.com/kaizen_nagoya/items/ae391b77fffb098b8fb4

プログラマの「プログラムが書ける」思い込みは強みだ。３つの理由。仮説（168）統計と確率(17) , OSEK(79)
https://qiita.com/kaizen_nagoya/items/bc5dd86e414de402ec29

出力(output)と呼ばないで。これは状態(state)です。
https://qiita.com/kaizen_nagoya/items/80b8b5913b2748867840

これからの情報伝達手段の在り方について考えてみよう。炎上と便乗。
https://qiita.com/kaizen_nagoya/items/71a09077ac195214f0db

ISO/IEC JTC1 SC7 Software and System Engineering
https://qiita.com/kaizen_nagoya/items/48b43f0f6976a078d907

アクセシビリティの知見を発信しよう！（再び）
https://qiita.com/kaizen_nagoya/items/03457eb9ee74105ee618

統計論及確率論輪講（再び）
https://qiita.com/kaizen_nagoya/items/590874ccfca988e85ea3

読者の心をグッと惹き寄せる7つの魔法
https://qiita.com/kaizen_nagoya/items/b1b5e89bd5c0a211d862

「@kazuo_reve 新人の方によく展開している有益な情報」確認一覧
https://qiita.com/kaizen_nagoya/items/b9380888d1e5a042646b

ソースコードで議論しよう。日本語で議論するの止めましょう（あるプログラミング技術の議論報告）
https://qiita.com/kaizen_nagoya/items/8b9811c80f3338c6c0b0

脳内コンパイラの3つの危険
https://qiita.com/kaizen_nagoya/items/7025cf2d7bd9f276e382

心理学の本を読むよりはコンパイラ書いた方がよくね。仮説（34）
https://qiita.com/kaizen_nagoya/items/fa715732cc148e48880e

NASAを超えるつもりがあれば読んでください。
https://qiita.com/kaizen_nagoya/items/e81669f9cb53109157f6

データサイエンティストの気づき!「勉強して仕事に役立てない人。大嫌い!!」『それ自分かも?』ってなった!!! 
https://qiita.com/kaizen_nagoya/items/d85830d58d8dd7f71d07

「ぼくの好きな先生」「人がやらないことをやれ」プログラマになるまで。仮説（37）　
https://qiita.com/kaizen_nagoya/items/53e4bded9fe5f724b3c4

なぜ経済学徒を辞め、計算機屋になったか（経済学部入学前・入学後・卒業後対応） 転職(1)  
https://qiita.com/kaizen_nagoya/items/06335a1d24c099733f64    

プログラミング言語教育のXYZ。 仮説（52） 
https://qiita.com/kaizen_nagoya/items/1950c5810fb5c0b07be4

【24卒向け】9ヶ月後に年収１０００万円を目指す。二つの関門と三つの道。
https://qiita.com/kaizen_nagoya/items/fb5bff147193f726ad25

「【25卒向け】Qiita Career Meetup for STUDENT」予習の勧め
https://qiita.com/kaizen_nagoya/items/00eadb8a6e738cb6336f

大学入試不合格でも筆記試験のない大学に入って卒業できる。卒業しなくても博士になれる。
https://qiita.com/kaizen_nagoya/items/74adec99f396d64b5fd5

全世界の不登校の子供たち「博士論文」を書こう。世界子供博士論文遠隔実践中心 安全(99)
https://qiita.com/kaizen_nagoya/items/912d69032c012bcc84f2

小川メソッド　覚え（書きかけ）
https://qiita.com/kaizen_nagoya/items/3593d72eca551742df68

DoCAP（ドゥーキャップ）って何ですか？
https://qiita.com/kaizen_nagoya/items/47e0e6509ab792c43327

views 20,000越え自己記事一覧
https://qiita.com/kaizen_nagoya/items/58e8bd6450957cdecd81

Views１万越え、もうすぐ１万記事一覧 最近いいねをいただいた213記事
https://qiita.com/kaizen_nagoya/items/d2b805717a92459ce853

amazon 殿堂入りNo1レビュアになるまで。仮説(102)
https://qiita.com/kaizen_nagoya/items/83259d18921ce75a91f4

100以上いいねをいただいた記事16選
https://qiita.com/kaizen_nagoya/items/f8d958d9084ffbd15d2a

小川清最終講義、最終講義（再）計画, Ethernet(100) 英語(100) 安全(100)
https://qiita.com/kaizen_nagoya/items/e2df642e3951e35e6a53

＜この記事は個人の過去の経験に基づく個人の感想です。現在所属する組織、業務とは関係がありません。＞
This article is an individual impression based on my individual experience. It has nothing to do with the organization or business to which I currently belong.
### 文書履歴(document history)
ver. 0.10 初稿 20180928
ver. 0.11 参考文献追記 20181019
### 最後までおよみいただきありがとうございました。
いいね　💚、フォローをお願いします。
#### Thank you very much for reading to the last sentence.
Please press the like icon 💚　and follow me for your happy life.