PackagesNotFoundError: 今日のconda error(8)

dockerで機械学習書籍・ソースM2:macOS確認中。
https://qiita.com/kaizen_nagoya/items/887fa4a2ce9a7f90ca0f

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

を再現中に出たエラー

```ubuntu:bash
# conda install quiver_engine                         
Collecting package metadata (current_repodata.json): done                       
Solving environment: unsuccessful initial attempt using frozen solve. Retrying with flexible solve.                         
Collecting package metadata (repodata.json): done
Solving environment: unsuccessful initial attempt using frozen solve. Retrying with flexible solve.

PackagesNotFoundError: The following packages are not available from current channels:

  - quiver_engine

Current channels:

  - https://repo.anaconda.com/pkgs/main/linux-aarch64
  - https://repo.anaconda.com/pkgs/main/noarch
  - https://repo.anaconda.com/pkgs/r/linux-aarch64
  - https://repo.anaconda.com/pkgs/r/noarch

To search for alternate channels that may provide the conda package you're
looking for, navigate to

    https://anaconda.org

and use the search bar at the top of the page.
```

# conda 
https://anaconda.org

検索するのにログインが必要。
Anacondaに登録した。
これまでながらくお世話になってきた。
感謝しかない。

公AnacondaのDockerがすごい。apt update; apt upgradeが何もないってすごい。毎日更新しているのだろうか。

## quiver_engine

| Favorites|	 Downloads|	 Artifact (owner / artifact)|	Platforms|
|:--|:--|:--|:--|
|0|	1504|anaconda / quiver_engine 0.1.4.1.4 Interactive per-layer visualization for convents in keras conda|linux-64 osx-64|
|0|	146| main / quiver_engine 0.1.4.1.4 Interactive per-layer visualization for convents in keras conda|	linux-64 osx-64|
|0|	46|	 jjh_cio_testing / quiver_engine Interactive per-layer visualization for convents in keras conda|	linux-64|

https://anaconda.org/anaconda/quiver_engine

>conda install -c anaconda quiver_engine

```ubuntu:bash
# conda install -c anaconda quiver_engine
Collecting package metadata (current_repodata.json): done
Solving environment: unsuccessful initial attempt using frozen solve. Retrying with flexible solve.
Collecting package metadata (repodata.json): done
Solving environment: unsuccessful initial attempt using frozen solve. Retrying with flexible solve.

PackagesNotFoundError: The following packages are not available from current channels:

  - quiver_engine

Current channels:

  - https://conda.anaconda.org/anaconda/linux-aarch64
  - https://conda.anaconda.org/anaconda/noarch
  - https://repo.anaconda.com/pkgs/main/linux-aarch64
  - https://repo.anaconda.com/pkgs/main/noarch
  - https://repo.anaconda.com/pkgs/r/linux-aarch64
  - https://repo.anaconda.com/pkgs/r/noarch

To search for alternate channels that may provide the conda package you're
looking for, navigate to

    https://anaconda.org

and use the search bar at the top of the page.
```
https://keplr-io.github.io/quiver/

```ubuntu:bash
#  pip install quiver_engine
Collecting quiver_engine
  Downloading quiver_engine-0.1.4.1.4.tar.gz (398 kB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 398.7/398.7 kB 5.1 MB/s eta 0:00:00
  Preparing metadata (setup.py) ... error
  error: subprocess-exited-with-error
  
  × python setup.py egg_info did not run successfully.
  │ exit code: 1
  ╰─> [1 lines of output]
      error in quiver_engine setup command: "values of 'package_data' dict" must be a list of strings (got 'quiverboard/dist/*')
      [end of output]
  
  note: This error originates from a subprocess, and is likely not a problem with pip.
error: metadata-generation-failed

× Encountered error while generating package metadata.
╰─> See above for output.

note: This is an issue with the package mentioned above, not pip.
hint: See above for details.
```

```ubuntu:bash
(base) root@4842d75f325d:/# ls
bin   deep-learning-with-keras-ja  etc	 lib	mnt  proc  run	 srv  tmp  var
boot  dev			   home  media	opt  root  sbin  sys  usr
(base) root@4842d75f325d:/# cd deep-learning-with-keras-ja/
(base) root@4842d75f325d:/deep-learning-with-keras-ja# ls
README.md  ch02  ch04  ch06  ch08
ch01	   ch03  ch05  ch07  deep-learning-with-keras-ja.png
(base) root@4842d75f325d:/deep-learning-with-keras-ja# cd ch01
(base) root@4842d75f325d:/deep-learning-with-keras-ja/ch01# ls
keras_MINST_V1.py  keras_MINST_V3.py  make_tensorboard.py  requirements_gpu.txt
keras_MINST_V2.py  keras_MINST_V4.py  requirements.txt
(base) root@4842d75f325d:/deep-learning-with-keras-ja/ch01# python keras_MINST_V1.py 
Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz
11490434/11490434 [==============================] - 1s 0us/step
60000 train samples
10000 test samples
Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 dense (Dense)               (None, 10)                7850      
                                                                 
 activation (Activation)     (None, 10)                0         
                                                                 
=================================================================
Total params: 7,850
Trainable params: 7,850
Non-trainable params: 0
_________________________________________________________________
Epoch 1/200
375/375 [==============================] - 1s 1ms/step - loss: 1.3734 - accuracy: 0.6789 - val_loss: 0.8889 - val_accuracy: 0.8277
Epoch 2/200
375/375 [==============================] - 0s 699us/step - loss: 0.7901 - accuracy: 0.8283 - val_loss: 0.6550 - val_accuracy: 0.8580
Epoch 3/200
375/375 [==============================] - 0s 696us/step - loss: 0.6422 - accuracy: 0.8502 - val_loss: 0.5608 - val_accuracy: 0.8678
Epoch 4/200
375/375 [==============================] - 0s 685us/step - loss: 0.5706 - accuracy: 0.8608 - val_loss: 0.5085 - val_accuracy: 0.8749
Epoch 5/200
375/375 [==============================] - 0s 679us/step - loss: 0.5269 - accuracy: 0.8677 - val_loss: 0.4747 - val_accuracy: 0.8804
Epoch 6/200
375/375 [==============================] - 0s 668us/step - loss: 0.4969 - accuracy: 0.8726 - val_loss: 0.4507 - val_accuracy: 0.8862
Epoch 7/200
375/375 [==============================] - 0s 669us/step - loss: 0.4745 - accuracy: 0.8770 - val_loss: 0.4326 - val_accuracy: 0.8888
Epoch 8/200
375/375 [==============================] - 0s 677us/step - loss: 0.4573 - accuracy: 0.8804 - val_loss: 0.4187 - val_accuracy: 0.8913
Epoch 9/200
375/375 [==============================] - 0s 678us/step - loss: 0.4433 - accuracy: 0.8825 - val_loss: 0.4069 - val_accuracy: 0.8940
Epoch 10/200
375/375 [==============================] - 0s 689us/step - loss: 0.4318 - accuracy: 0.8851 - val_loss: 0.3973 - val_accuracy: 0.8964
Epoch 11/200
375/375 [==============================] - 0s 693us/step - loss: 0.4220 - accuracy: 0.8869 - val_loss: 0.3892 - val_accuracy: 0.8977
Epoch 12/200
375/375 [==============================] - 0s 690us/step - loss: 0.4136 - accuracy: 0.8884 - val_loss: 0.3822 - val_accuracy: 0.8992
Epoch 13/200
375/375 [==============================] - 0s 689us/step - loss: 0.4062 - accuracy: 0.8901 - val_loss: 0.3763 - val_accuracy: 0.9003
Epoch 14/200
375/375 [==============================] - 0s 691us/step - loss: 0.3998 - accuracy: 0.8915 - val_loss: 0.3709 - val_accuracy: 0.9012
Epoch 15/200
375/375 [==============================] - 0s 693us/step - loss: 0.3940 - accuracy: 0.8928 - val_loss: 0.3661 - val_accuracy: 0.9023
Epoch 16/200
375/375 [==============================] - 0s 692us/step - loss: 0.3888 - accuracy: 0.8934 - val_loss: 0.3619 - val_accuracy: 0.9032
Epoch 17/200
375/375 [==============================] - 0s 691us/step - loss: 0.3841 - accuracy: 0.8949 - val_loss: 0.3578 - val_accuracy: 0.9038
Epoch 18/200
375/375 [==============================] - 0s 690us/step - loss: 0.3798 - accuracy: 0.8957 - val_loss: 0.3543 - val_accuracy: 0.9045
Epoch 19/200
375/375 [==============================] - 0s 693us/step - loss: 0.3760 - accuracy: 0.8965 - val_loss: 0.3510 - val_accuracy: 0.9055
Epoch 20/200
375/375 [==============================] - 0s 696us/step - loss: 0.3723 - accuracy: 0.8973 - val_loss: 0.3481 - val_accuracy: 0.9059
Epoch 21/200
375/375 [==============================] - 0s 697us/step - loss: 0.3690 - accuracy: 0.8981 - val_loss: 0.3454 - val_accuracy: 0.9057
Epoch 22/200
375/375 [==============================] - 0s 733us/step - loss: 0.3659 - accuracy: 0.8988 - val_loss: 0.3428 - val_accuracy: 0.9062
Epoch 23/200
375/375 [==============================] - 0s 695us/step - loss: 0.3630 - accuracy: 0.8994 - val_loss: 0.3405 - val_accuracy: 0.9069
Epoch 24/200
375/375 [==============================] - 0s 694us/step - loss: 0.3603 - accuracy: 0.8998 - val_loss: 0.3382 - val_accuracy: 0.9072
Epoch 25/200
375/375 [==============================] - 0s 729us/step - loss: 0.3577 - accuracy: 0.9004 - val_loss: 0.3363 - val_accuracy: 0.9076
Epoch 26/200
375/375 [==============================] - 0s 698us/step - loss: 0.3553 - accuracy: 0.9011 - val_loss: 0.3343 - val_accuracy: 0.9081
Epoch 27/200
375/375 [==============================] - 0s 722us/step - loss: 0.3531 - accuracy: 0.9017 - val_loss: 0.3325 - val_accuracy: 0.9085
Epoch 28/200
375/375 [==============================] - 0s 691us/step - loss: 0.3510 - accuracy: 0.9021 - val_loss: 0.3307 - val_accuracy: 0.9095
Epoch 29/200
375/375 [==============================] - 0s 688us/step - loss: 0.3489 - accuracy: 0.9024 - val_loss: 0.3289 - val_accuracy: 0.9099
Epoch 30/200
375/375 [==============================] - 0s 695us/step - loss: 0.3470 - accuracy: 0.9034 - val_loss: 0.3274 - val_accuracy: 0.9099
Epoch 31/200
375/375 [==============================] - 0s 703us/step - loss: 0.3452 - accuracy: 0.9034 - val_loss: 0.3260 - val_accuracy: 0.9112
Epoch 32/200
375/375 [==============================] - 0s 711us/step - loss: 0.3434 - accuracy: 0.9040 - val_loss: 0.3247 - val_accuracy: 0.9107
Epoch 33/200
375/375 [==============================] - 0s 682us/step - loss: 0.3418 - accuracy: 0.9043 - val_loss: 0.3231 - val_accuracy: 0.9111
Epoch 34/200
375/375 [==============================] - 0s 689us/step - loss: 0.3402 - accuracy: 0.9044 - val_loss: 0.3219 - val_accuracy: 0.9115
Epoch 35/200
375/375 [==============================] - 0s 693us/step - loss: 0.3387 - accuracy: 0.9051 - val_loss: 0.3206 - val_accuracy: 0.9120
Epoch 36/200
375/375 [==============================] - 0s 692us/step - loss: 0.3372 - accuracy: 0.9058 - val_loss: 0.3196 - val_accuracy: 0.9120
Epoch 37/200
375/375 [==============================] - 0s 686us/step - loss: 0.3358 - accuracy: 0.9058 - val_loss: 0.3184 - val_accuracy: 0.9124
Epoch 38/200
375/375 [==============================] - 0s 694us/step - loss: 0.3345 - accuracy: 0.9062 - val_loss: 0.3173 - val_accuracy: 0.9122
Epoch 39/200
375/375 [==============================] - 0s 692us/step - loss: 0.3332 - accuracy: 0.9064 - val_loss: 0.3163 - val_accuracy: 0.9123
Epoch 40/200
375/375 [==============================] - 0s 701us/step - loss: 0.3320 - accuracy: 0.9068 - val_loss: 0.3154 - val_accuracy: 0.9125
Epoch 41/200
375/375 [==============================] - 0s 685us/step - loss: 0.3308 - accuracy: 0.9072 - val_loss: 0.3144 - val_accuracy: 0.9127
Epoch 42/200
375/375 [==============================] - 0s 689us/step - loss: 0.3296 - accuracy: 0.9073 - val_loss: 0.3135 - val_accuracy: 0.9133
Epoch 43/200
375/375 [==============================] - 0s 697us/step - loss: 0.3285 - accuracy: 0.9081 - val_loss: 0.3126 - val_accuracy: 0.9127
Epoch 44/200
375/375 [==============================] - 0s 693us/step - loss: 0.3274 - accuracy: 0.9082 - val_loss: 0.3118 - val_accuracy: 0.9128
Epoch 45/200
375/375 [==============================] - 0s 688us/step - loss: 0.3264 - accuracy: 0.9087 - val_loss: 0.3109 - val_accuracy: 0.9132
Epoch 46/200
375/375 [==============================] - 0s 690us/step - loss: 0.3254 - accuracy: 0.9089 - val_loss: 0.3101 - val_accuracy: 0.9132
Epoch 47/200
375/375 [==============================] - 0s 693us/step - loss: 0.3244 - accuracy: 0.9091 - val_loss: 0.3094 - val_accuracy: 0.9137
Epoch 48/200
375/375 [==============================] - 0s 692us/step - loss: 0.3235 - accuracy: 0.9094 - val_loss: 0.3087 - val_accuracy: 0.9136
Epoch 49/200
375/375 [==============================] - 0s 689us/step - loss: 0.3225 - accuracy: 0.9098 - val_loss: 0.3081 - val_accuracy: 0.9135
Epoch 50/200
375/375 [==============================] - 0s 687us/step - loss: 0.3217 - accuracy: 0.9098 - val_loss: 0.3072 - val_accuracy: 0.9142
Epoch 51/200
375/375 [==============================] - 0s 699us/step - loss: 0.3207 - accuracy: 0.9099 - val_loss: 0.3067 - val_accuracy: 0.9143
Epoch 52/200
375/375 [==============================] - 0s 696us/step - loss: 0.3200 - accuracy: 0.9108 - val_loss: 0.3059 - val_accuracy: 0.9142
Epoch 53/200
375/375 [==============================] - 0s 693us/step - loss: 0.3191 - accuracy: 0.9110 - val_loss: 0.3053 - val_accuracy: 0.9143
Epoch 54/200
375/375 [==============================] - 0s 737us/step - loss: 0.3184 - accuracy: 0.9108 - val_loss: 0.3047 - val_accuracy: 0.9143
Epoch 55/200
375/375 [==============================] - 0s 697us/step - loss: 0.3176 - accuracy: 0.9109 - val_loss: 0.3040 - val_accuracy: 0.9147
Epoch 56/200
375/375 [==============================] - 0s 692us/step - loss: 0.3168 - accuracy: 0.9115 - val_loss: 0.3035 - val_accuracy: 0.9143
Epoch 57/200
375/375 [==============================] - 0s 695us/step - loss: 0.3161 - accuracy: 0.9115 - val_loss: 0.3029 - val_accuracy: 0.9152
Epoch 58/200
375/375 [==============================] - 0s 691us/step - loss: 0.3154 - accuracy: 0.9121 - val_loss: 0.3024 - val_accuracy: 0.9153
Epoch 59/200
375/375 [==============================] - 0s 706us/step - loss: 0.3147 - accuracy: 0.9121 - val_loss: 0.3018 - val_accuracy: 0.9151
Epoch 60/200
375/375 [==============================] - 0s 732us/step - loss: 0.3140 - accuracy: 0.9125 - val_loss: 0.3013 - val_accuracy: 0.9154
Epoch 61/200
375/375 [==============================] - 0s 702us/step - loss: 0.3134 - accuracy: 0.9126 - val_loss: 0.3008 - val_accuracy: 0.9151
Epoch 62/200
375/375 [==============================] - 0s 696us/step - loss: 0.3127 - accuracy: 0.9126 - val_loss: 0.3003 - val_accuracy: 0.9153
Epoch 63/200
375/375 [==============================] - 0s 707us/step - loss: 0.3121 - accuracy: 0.9132 - val_loss: 0.2998 - val_accuracy: 0.9153
Epoch 64/200
375/375 [==============================] - 0s 700us/step - loss: 0.3115 - accuracy: 0.9133 - val_loss: 0.2994 - val_accuracy: 0.9160
Epoch 65/200
375/375 [==============================] - 0s 696us/step - loss: 0.3108 - accuracy: 0.9134 - val_loss: 0.2989 - val_accuracy: 0.9157
Epoch 66/200
375/375 [==============================] - 0s 696us/step - loss: 0.3103 - accuracy: 0.9136 - val_loss: 0.2984 - val_accuracy: 0.9158
Epoch 67/200
375/375 [==============================] - 0s 695us/step - loss: 0.3097 - accuracy: 0.9137 - val_loss: 0.2980 - val_accuracy: 0.9164
Epoch 68/200
375/375 [==============================] - 0s 699us/step - loss: 0.3091 - accuracy: 0.9138 - val_loss: 0.2976 - val_accuracy: 0.9163
Epoch 69/200
375/375 [==============================] - 0s 691us/step - loss: 0.3086 - accuracy: 0.9141 - val_loss: 0.2972 - val_accuracy: 0.9160
Epoch 70/200
375/375 [==============================] - 0s 758us/step - loss: 0.3080 - accuracy: 0.9144 - val_loss: 0.2967 - val_accuracy: 0.9166
Epoch 71/200
375/375 [==============================] - 0s 698us/step - loss: 0.3075 - accuracy: 0.9145 - val_loss: 0.2964 - val_accuracy: 0.9170
Epoch 72/200
375/375 [==============================] - 0s 700us/step - loss: 0.3069 - accuracy: 0.9148 - val_loss: 0.2960 - val_accuracy: 0.9169
Epoch 73/200
375/375 [==============================] - 0s 692us/step - loss: 0.3065 - accuracy: 0.9146 - val_loss: 0.2956 - val_accuracy: 0.9167
Epoch 74/200
375/375 [==============================] - 0s 699us/step - loss: 0.3060 - accuracy: 0.9147 - val_loss: 0.2952 - val_accuracy: 0.9170
Epoch 75/200
375/375 [==============================] - 0s 697us/step - loss: 0.3055 - accuracy: 0.9151 - val_loss: 0.2949 - val_accuracy: 0.9168
Epoch 76/200
375/375 [==============================] - 0s 698us/step - loss: 0.3050 - accuracy: 0.9152 - val_loss: 0.2945 - val_accuracy: 0.9168
Epoch 77/200
375/375 [==============================] - 0s 694us/step - loss: 0.3045 - accuracy: 0.9153 - val_loss: 0.2941 - val_accuracy: 0.9171
Epoch 78/200
375/375 [==============================] - 0s 705us/step - loss: 0.3041 - accuracy: 0.9153 - val_loss: 0.2938 - val_accuracy: 0.9173
Epoch 79/200
375/375 [==============================] - 0s 701us/step - loss: 0.3036 - accuracy: 0.9155 - val_loss: 0.2934 - val_accuracy: 0.9174
Epoch 80/200
375/375 [==============================] - 0s 702us/step - loss: 0.3032 - accuracy: 0.9154 - val_loss: 0.2931 - val_accuracy: 0.9170
Epoch 81/200
375/375 [==============================] - 0s 695us/step - loss: 0.3027 - accuracy: 0.9159 - val_loss: 0.2928 - val_accuracy: 0.9174
Epoch 82/200
375/375 [==============================] - 0s 694us/step - loss: 0.3023 - accuracy: 0.9157 - val_loss: 0.2925 - val_accuracy: 0.9178
Epoch 83/200
375/375 [==============================] - 0s 699us/step - loss: 0.3018 - accuracy: 0.9159 - val_loss: 0.2922 - val_accuracy: 0.9183
Epoch 84/200
375/375 [==============================] - 0s 694us/step - loss: 0.3015 - accuracy: 0.9159 - val_loss: 0.2919 - val_accuracy: 0.9182
Epoch 85/200
375/375 [==============================] - 0s 693us/step - loss: 0.3010 - accuracy: 0.9159 - val_loss: 0.2916 - val_accuracy: 0.9176
Epoch 86/200
375/375 [==============================] - 0s 695us/step - loss: 0.3006 - accuracy: 0.9161 - val_loss: 0.2913 - val_accuracy: 0.9179
Epoch 87/200
375/375 [==============================] - 0s 700us/step - loss: 0.3002 - accuracy: 0.9163 - val_loss: 0.2911 - val_accuracy: 0.9181
Epoch 88/200
375/375 [==============================] - 0s 693us/step - loss: 0.2999 - accuracy: 0.9161 - val_loss: 0.2908 - val_accuracy: 0.9189
Epoch 89/200
375/375 [==============================] - 0s 694us/step - loss: 0.2995 - accuracy: 0.9159 - val_loss: 0.2905 - val_accuracy: 0.9188
Epoch 90/200
375/375 [==============================] - 0s 692us/step - loss: 0.2991 - accuracy: 0.9164 - val_loss: 0.2902 - val_accuracy: 0.9187
Epoch 91/200
375/375 [==============================] - 0s 701us/step - loss: 0.2987 - accuracy: 0.9162 - val_loss: 0.2900 - val_accuracy: 0.9187
Epoch 92/200
375/375 [==============================] - 0s 696us/step - loss: 0.2984 - accuracy: 0.9163 - val_loss: 0.2896 - val_accuracy: 0.9188
Epoch 93/200
375/375 [==============================] - 0s 702us/step - loss: 0.2980 - accuracy: 0.9168 - val_loss: 0.2894 - val_accuracy: 0.9189
Epoch 94/200
375/375 [==============================] - 0s 694us/step - loss: 0.2977 - accuracy: 0.9168 - val_loss: 0.2892 - val_accuracy: 0.9190
Epoch 95/200
375/375 [==============================] - 0s 691us/step - loss: 0.2973 - accuracy: 0.9168 - val_loss: 0.2890 - val_accuracy: 0.9191
Epoch 96/200
375/375 [==============================] - 0s 700us/step - loss: 0.2970 - accuracy: 0.9165 - val_loss: 0.2887 - val_accuracy: 0.9194
Epoch 97/200
375/375 [==============================] - 0s 699us/step - loss: 0.2966 - accuracy: 0.9169 - val_loss: 0.2884 - val_accuracy: 0.9187
Epoch 98/200
375/375 [==============================] - 0s 733us/step - loss: 0.2963 - accuracy: 0.9170 - val_loss: 0.2882 - val_accuracy: 0.9191
Epoch 99/200
375/375 [==============================] - 0s 701us/step - loss: 0.2960 - accuracy: 0.9171 - val_loss: 0.2879 - val_accuracy: 0.9193
Epoch 100/200
375/375 [==============================] - 0s 698us/step - loss: 0.2956 - accuracy: 0.9175 - val_loss: 0.2878 - val_accuracy: 0.9194
Epoch 101/200
375/375 [==============================] - 0s 728us/step - loss: 0.2953 - accuracy: 0.9172 - val_loss: 0.2875 - val_accuracy: 0.9194
Epoch 102/200
375/375 [==============================] - 0s 702us/step - loss: 0.2950 - accuracy: 0.9175 - val_loss: 0.2874 - val_accuracy: 0.9194
Epoch 103/200
375/375 [==============================] - 0s 724us/step - loss: 0.2947 - accuracy: 0.9175 - val_loss: 0.2870 - val_accuracy: 0.9196
Epoch 104/200
375/375 [==============================] - 0s 698us/step - loss: 0.2944 - accuracy: 0.9177 - val_loss: 0.2869 - val_accuracy: 0.9195
Epoch 105/200
375/375 [==============================] - 0s 692us/step - loss: 0.2941 - accuracy: 0.9181 - val_loss: 0.2867 - val_accuracy: 0.9201
Epoch 106/200
375/375 [==============================] - 0s 700us/step - loss: 0.2938 - accuracy: 0.9180 - val_loss: 0.2865 - val_accuracy: 0.9197
Epoch 107/200
375/375 [==============================] - 0s 693us/step - loss: 0.2935 - accuracy: 0.9180 - val_loss: 0.2862 - val_accuracy: 0.9197
Epoch 108/200
375/375 [==============================] - 0s 730us/step - loss: 0.2932 - accuracy: 0.9182 - val_loss: 0.2860 - val_accuracy: 0.9202
Epoch 109/200
375/375 [==============================] - 0s 691us/step - loss: 0.2929 - accuracy: 0.9185 - val_loss: 0.2859 - val_accuracy: 0.9198
Epoch 110/200
375/375 [==============================] - 0s 696us/step - loss: 0.2926 - accuracy: 0.9182 - val_loss: 0.2856 - val_accuracy: 0.9203
Epoch 111/200
375/375 [==============================] - 0s 697us/step - loss: 0.2924 - accuracy: 0.9185 - val_loss: 0.2854 - val_accuracy: 0.9201
Epoch 112/200
375/375 [==============================] - 0s 700us/step - loss: 0.2921 - accuracy: 0.9187 - val_loss: 0.2853 - val_accuracy: 0.9197
Epoch 113/200
375/375 [==============================] - 0s 689us/step - loss: 0.2918 - accuracy: 0.9186 - val_loss: 0.2851 - val_accuracy: 0.9201
Epoch 114/200
375/375 [==============================] - 0s 696us/step - loss: 0.2915 - accuracy: 0.9189 - val_loss: 0.2849 - val_accuracy: 0.9198
Epoch 115/200
375/375 [==============================] - 0s 696us/step - loss: 0.2913 - accuracy: 0.9187 - val_loss: 0.2848 - val_accuracy: 0.9205
Epoch 116/200
375/375 [==============================] - 0s 705us/step - loss: 0.2910 - accuracy: 0.9191 - val_loss: 0.2845 - val_accuracy: 0.9203
Epoch 117/200
375/375 [==============================] - 0s 694us/step - loss: 0.2908 - accuracy: 0.9190 - val_loss: 0.2844 - val_accuracy: 0.9206
Epoch 118/200
375/375 [==============================] - 0s 697us/step - loss: 0.2905 - accuracy: 0.9191 - val_loss: 0.2842 - val_accuracy: 0.9199
Epoch 119/200
375/375 [==============================] - 0s 695us/step - loss: 0.2902 - accuracy: 0.9191 - val_loss: 0.2841 - val_accuracy: 0.9206
Epoch 120/200
375/375 [==============================] - 0s 698us/step - loss: 0.2900 - accuracy: 0.9193 - val_loss: 0.2839 - val_accuracy: 0.9207
Epoch 121/200
375/375 [==============================] - 0s 690us/step - loss: 0.2898 - accuracy: 0.9193 - val_loss: 0.2838 - val_accuracy: 0.9207
Epoch 122/200
375/375 [==============================] - 0s 693us/step - loss: 0.2895 - accuracy: 0.9194 - val_loss: 0.2836 - val_accuracy: 0.9208
Epoch 123/200
375/375 [==============================] - 0s 694us/step - loss: 0.2893 - accuracy: 0.9193 - val_loss: 0.2834 - val_accuracy: 0.9212
Epoch 124/200
375/375 [==============================] - 0s 696us/step - loss: 0.2891 - accuracy: 0.9198 - val_loss: 0.2832 - val_accuracy: 0.9204
Epoch 125/200
375/375 [==============================] - 0s 696us/step - loss: 0.2888 - accuracy: 0.9197 - val_loss: 0.2830 - val_accuracy: 0.9211
Epoch 126/200
375/375 [==============================] - 0s 692us/step - loss: 0.2886 - accuracy: 0.9200 - val_loss: 0.2829 - val_accuracy: 0.9212
Epoch 127/200
375/375 [==============================] - 0s 694us/step - loss: 0.2884 - accuracy: 0.9199 - val_loss: 0.2827 - val_accuracy: 0.9212
Epoch 128/200
375/375 [==============================] - 0s 695us/step - loss: 0.2881 - accuracy: 0.9199 - val_loss: 0.2826 - val_accuracy: 0.9212
Epoch 129/200
375/375 [==============================] - 0s 694us/step - loss: 0.2879 - accuracy: 0.9198 - val_loss: 0.2824 - val_accuracy: 0.9212
Epoch 130/200
375/375 [==============================] - 0s 690us/step - loss: 0.2876 - accuracy: 0.9199 - val_loss: 0.2824 - val_accuracy: 0.9210
Epoch 131/200
375/375 [==============================] - 0s 698us/step - loss: 0.2875 - accuracy: 0.9200 - val_loss: 0.2821 - val_accuracy: 0.9212
Epoch 132/200
375/375 [==============================] - 0s 695us/step - loss: 0.2872 - accuracy: 0.9202 - val_loss: 0.2821 - val_accuracy: 0.9216
Epoch 133/200
375/375 [==============================] - 0s 693us/step - loss: 0.2870 - accuracy: 0.9202 - val_loss: 0.2818 - val_accuracy: 0.9212
Epoch 134/200
375/375 [==============================] - 0s 691us/step - loss: 0.2868 - accuracy: 0.9205 - val_loss: 0.2817 - val_accuracy: 0.9218
Epoch 135/200
375/375 [==============================] - 0s 704us/step - loss: 0.2866 - accuracy: 0.9201 - val_loss: 0.2815 - val_accuracy: 0.9208
Epoch 136/200
375/375 [==============================] - 0s 730us/step - loss: 0.2864 - accuracy: 0.9203 - val_loss: 0.2815 - val_accuracy: 0.9212
Epoch 137/200
375/375 [==============================] - 0s 697us/step - loss: 0.2861 - accuracy: 0.9203 - val_loss: 0.2814 - val_accuracy: 0.9206
Epoch 138/200
375/375 [==============================] - 0s 696us/step - loss: 0.2860 - accuracy: 0.9205 - val_loss: 0.2812 - val_accuracy: 0.9214
Epoch 139/200
375/375 [==============================] - 0s 704us/step - loss: 0.2858 - accuracy: 0.9206 - val_loss: 0.2810 - val_accuracy: 0.9211
Epoch 140/200
375/375 [==============================] - 0s 701us/step - loss: 0.2856 - accuracy: 0.9206 - val_loss: 0.2810 - val_accuracy: 0.9213
Epoch 141/200
375/375 [==============================] - 0s 694us/step - loss: 0.2854 - accuracy: 0.9207 - val_loss: 0.2808 - val_accuracy: 0.9219
Epoch 142/200
375/375 [==============================] - 0s 697us/step - loss: 0.2852 - accuracy: 0.9208 - val_loss: 0.2807 - val_accuracy: 0.9215
Epoch 143/200
375/375 [==============================] - 0s 696us/step - loss: 0.2850 - accuracy: 0.9209 - val_loss: 0.2805 - val_accuracy: 0.9216
Epoch 144/200
375/375 [==============================] - 0s 698us/step - loss: 0.2848 - accuracy: 0.9207 - val_loss: 0.2804 - val_accuracy: 0.9219
Epoch 145/200
375/375 [==============================] - 0s 697us/step - loss: 0.2846 - accuracy: 0.9210 - val_loss: 0.2804 - val_accuracy: 0.9221
Epoch 146/200
375/375 [==============================] - 0s 757us/step - loss: 0.2844 - accuracy: 0.9210 - val_loss: 0.2802 - val_accuracy: 0.9220
Epoch 147/200
375/375 [==============================] - 0s 687us/step - loss: 0.2842 - accuracy: 0.9211 - val_loss: 0.2801 - val_accuracy: 0.9218
Epoch 148/200
375/375 [==============================] - 0s 697us/step - loss: 0.2841 - accuracy: 0.9210 - val_loss: 0.2799 - val_accuracy: 0.9214
Epoch 149/200
375/375 [==============================] - 0s 696us/step - loss: 0.2838 - accuracy: 0.9210 - val_loss: 0.2799 - val_accuracy: 0.9220
Epoch 150/200
375/375 [==============================] - 0s 698us/step - loss: 0.2837 - accuracy: 0.9210 - val_loss: 0.2798 - val_accuracy: 0.9221
Epoch 151/200
375/375 [==============================] - 0s 694us/step - loss: 0.2834 - accuracy: 0.9209 - val_loss: 0.2797 - val_accuracy: 0.9221
Epoch 152/200
375/375 [==============================] - 0s 698us/step - loss: 0.2833 - accuracy: 0.9215 - val_loss: 0.2795 - val_accuracy: 0.9222
Epoch 153/200
375/375 [==============================] - 0s 694us/step - loss: 0.2832 - accuracy: 0.9216 - val_loss: 0.2794 - val_accuracy: 0.9222
Epoch 154/200
375/375 [==============================] - 0s 704us/step - loss: 0.2829 - accuracy: 0.9215 - val_loss: 0.2793 - val_accuracy: 0.9214
Epoch 155/200
375/375 [==============================] - 0s 698us/step - loss: 0.2828 - accuracy: 0.9216 - val_loss: 0.2792 - val_accuracy: 0.9217
Epoch 156/200
375/375 [==============================] - 0s 699us/step - loss: 0.2826 - accuracy: 0.9214 - val_loss: 0.2791 - val_accuracy: 0.9224
Epoch 157/200
375/375 [==============================] - 0s 693us/step - loss: 0.2824 - accuracy: 0.9216 - val_loss: 0.2789 - val_accuracy: 0.9220
Epoch 158/200
375/375 [==============================] - 0s 697us/step - loss: 0.2823 - accuracy: 0.9214 - val_loss: 0.2788 - val_accuracy: 0.9221
Epoch 159/200
375/375 [==============================] - 0s 697us/step - loss: 0.2821 - accuracy: 0.9220 - val_loss: 0.2787 - val_accuracy: 0.9223
Epoch 160/200
375/375 [==============================] - 0s 696us/step - loss: 0.2819 - accuracy: 0.9220 - val_loss: 0.2787 - val_accuracy: 0.9223
Epoch 161/200
375/375 [==============================] - 0s 697us/step - loss: 0.2818 - accuracy: 0.9217 - val_loss: 0.2785 - val_accuracy: 0.9224
Epoch 162/200
375/375 [==============================] - 0s 690us/step - loss: 0.2816 - accuracy: 0.9217 - val_loss: 0.2786 - val_accuracy: 0.9222
Epoch 163/200
375/375 [==============================] - 0s 696us/step - loss: 0.2814 - accuracy: 0.9219 - val_loss: 0.2783 - val_accuracy: 0.9224
Epoch 164/200
375/375 [==============================] - 0s 697us/step - loss: 0.2812 - accuracy: 0.9218 - val_loss: 0.2783 - val_accuracy: 0.9227
Epoch 165/200
375/375 [==============================] - 0s 694us/step - loss: 0.2811 - accuracy: 0.9220 - val_loss: 0.2781 - val_accuracy: 0.9222
Epoch 166/200
375/375 [==============================] - 0s 693us/step - loss: 0.2810 - accuracy: 0.9221 - val_loss: 0.2781 - val_accuracy: 0.9222
Epoch 167/200
375/375 [==============================] - 0s 694us/step - loss: 0.2808 - accuracy: 0.9221 - val_loss: 0.2780 - val_accuracy: 0.9224
Epoch 168/200
375/375 [==============================] - 0s 699us/step - loss: 0.2807 - accuracy: 0.9218 - val_loss: 0.2778 - val_accuracy: 0.9224
Epoch 169/200
375/375 [==============================] - 0s 697us/step - loss: 0.2805 - accuracy: 0.9222 - val_loss: 0.2777 - val_accuracy: 0.9221
Epoch 170/200
375/375 [==============================] - 0s 692us/step - loss: 0.2803 - accuracy: 0.9220 - val_loss: 0.2776 - val_accuracy: 0.9222
Epoch 171/200
375/375 [==============================] - 0s 697us/step - loss: 0.2802 - accuracy: 0.9225 - val_loss: 0.2776 - val_accuracy: 0.9227
Epoch 172/200
375/375 [==============================] - 0s 699us/step - loss: 0.2801 - accuracy: 0.9223 - val_loss: 0.2775 - val_accuracy: 0.9224
Epoch 173/200
375/375 [==============================] - 0s 708us/step - loss: 0.2799 - accuracy: 0.9223 - val_loss: 0.2774 - val_accuracy: 0.9222
Epoch 174/200
375/375 [==============================] - 0s 721us/step - loss: 0.2798 - accuracy: 0.9225 - val_loss: 0.2773 - val_accuracy: 0.9223
Epoch 175/200
375/375 [==============================] - 0s 702us/step - loss: 0.2796 - accuracy: 0.9222 - val_loss: 0.2772 - val_accuracy: 0.9223
Epoch 176/200
375/375 [==============================] - 0s 700us/step - loss: 0.2795 - accuracy: 0.9225 - val_loss: 0.2771 - val_accuracy: 0.9222
Epoch 177/200
375/375 [==============================] - 0s 731us/step - loss: 0.2793 - accuracy: 0.9226 - val_loss: 0.2771 - val_accuracy: 0.9227
Epoch 178/200
375/375 [==============================] - 0s 701us/step - loss: 0.2792 - accuracy: 0.9226 - val_loss: 0.2769 - val_accuracy: 0.9223
Epoch 179/200
375/375 [==============================] - 0s 729us/step - loss: 0.2790 - accuracy: 0.9222 - val_loss: 0.2769 - val_accuracy: 0.9227
Epoch 180/200
375/375 [==============================] - 0s 696us/step - loss: 0.2789 - accuracy: 0.9225 - val_loss: 0.2768 - val_accuracy: 0.9228
Epoch 181/200
375/375 [==============================] - 0s 692us/step - loss: 0.2788 - accuracy: 0.9225 - val_loss: 0.2767 - val_accuracy: 0.9222
Epoch 182/200
375/375 [==============================] - 0s 698us/step - loss: 0.2786 - accuracy: 0.9226 - val_loss: 0.2766 - val_accuracy: 0.9225
Epoch 183/200
375/375 [==============================] - 0s 698us/step - loss: 0.2785 - accuracy: 0.9228 - val_loss: 0.2766 - val_accuracy: 0.9227
Epoch 184/200
375/375 [==============================] - 0s 729us/step - loss: 0.2784 - accuracy: 0.9228 - val_loss: 0.2764 - val_accuracy: 0.9226
Epoch 185/200
375/375 [==============================] - 0s 693us/step - loss: 0.2782 - accuracy: 0.9226 - val_loss: 0.2764 - val_accuracy: 0.9223
Epoch 186/200
375/375 [==============================] - 0s 696us/step - loss: 0.2780 - accuracy: 0.9227 - val_loss: 0.2764 - val_accuracy: 0.9230
Epoch 187/200
375/375 [==============================] - 0s 698us/step - loss: 0.2780 - accuracy: 0.9229 - val_loss: 0.2762 - val_accuracy: 0.9227
Epoch 188/200
375/375 [==============================] - 0s 698us/step - loss: 0.2778 - accuracy: 0.9230 - val_loss: 0.2762 - val_accuracy: 0.9222
Epoch 189/200
375/375 [==============================] - 0s 691us/step - loss: 0.2776 - accuracy: 0.9228 - val_loss: 0.2761 - val_accuracy: 0.9227
Epoch 190/200
375/375 [==============================] - 0s 699us/step - loss: 0.2775 - accuracy: 0.9230 - val_loss: 0.2761 - val_accuracy: 0.9226
Epoch 191/200
375/375 [==============================] - 0s 693us/step - loss: 0.2773 - accuracy: 0.9230 - val_loss: 0.2761 - val_accuracy: 0.9227
Epoch 192/200
375/375 [==============================] - 0s 700us/step - loss: 0.2773 - accuracy: 0.9230 - val_loss: 0.2758 - val_accuracy: 0.9224
Epoch 193/200
375/375 [==============================] - 0s 693us/step - loss: 0.2771 - accuracy: 0.9231 - val_loss: 0.2758 - val_accuracy: 0.9224
Epoch 194/200
375/375 [==============================] - 0s 699us/step - loss: 0.2770 - accuracy: 0.9230 - val_loss: 0.2757 - val_accuracy: 0.9227
Epoch 195/200
375/375 [==============================] - 0s 698us/step - loss: 0.2769 - accuracy: 0.9230 - val_loss: 0.2756 - val_accuracy: 0.9227
Epoch 196/200
375/375 [==============================] - 0s 698us/step - loss: 0.2768 - accuracy: 0.9231 - val_loss: 0.2756 - val_accuracy: 0.9224
Epoch 197/200
375/375 [==============================] - 0s 691us/step - loss: 0.2766 - accuracy: 0.9229 - val_loss: 0.2755 - val_accuracy: 0.9228
Epoch 198/200
375/375 [==============================] - 0s 694us/step - loss: 0.2765 - accuracy: 0.9234 - val_loss: 0.2755 - val_accuracy: 0.9224
Epoch 199/200
375/375 [==============================] - 0s 697us/step - loss: 0.2764 - accuracy: 0.9231 - val_loss: 0.2754 - val_accuracy: 0.9224
Epoch 200/200
375/375 [==============================] - 0s 696us/step - loss: 0.2762 - accuracy: 0.9232 - val_loss: 0.2753 - val_accuracy: 0.9230
313/313 [==============================] - 0s 352us/step - loss: 0.2769 - accuracy: 0.9227

Test score: 0.27694398164749146
Test accuracy: 0.9226999878883362

# echo $SHELL
/bin/bash

```

時間は圧倒的には速くなった。

```macOS:bash
$ docker ps
CONTAINER ID   IMAGE                   COMMAND       CREATED       STATUS       PORTS                                            NAMES
4842d75f325d   continuumio/anaconda3   "/bin/bash"   8 hours ago   Up 8 hours   0.0.0.0:6066->6066/tcp, 0.0.0.0:8880->8880/tcp   festive_antonelli
$ docker commit 4842d75f325d  kaizenjapan/anaconda-tensorflow-m2macOS
invalid reference format: repository name must be lowercase
$ docker commit 4842d75f325d  kaizenjapan/anaconda-tensorflow-m2macos
sha256:ace660cd14b5370d65a5041c860167411557f1216dfe9b1c4d1cde0801e05619
$ docker push kaizenjapan/anaconda-tensoflow-m2macos
Using default tag: latest
The push refers to repository [docker.io/kaizenjapan/anaconda-tensoflow-m2macos]
An image does not exist locally with the tag: kaizenjapan/anaconda-tensoflow-m2macos
$ docker push kaizenjapan/anaconda-tensorflow-m2macos
Using default tag: latest
The push refers to repository [docker.io/kaizenjapan/anaconda-tensorflow-m2macos]
aa2163b9fe7a: Pushed 
8109b71a3b8d: Mounted from continuumio/anaconda3 
6c9ad649ba04: Mounted from continuumio/anaconda3 
latest: digest: sha256:4c98a386ee42a133dc378070fabeb0cb34af62a78b8d892df40b777b323dfaaa size: 956
```

保存しておいた。


# 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
## 自己記事一覧
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

「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

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

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

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

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

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

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

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

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

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/26bda595f341a27901a0

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

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

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

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

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

++ 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

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

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

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

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

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

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

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

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 the individual's 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 20231001
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