1.すぐに利用したい方へ(as soon as)

「Advanced Deep Learning with Keras」 By Philippe Remy

http://shop.oreilly.com/product/0636920154891.do

docker

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。

docker pull and run

$ docker pull kaizenjapan/anaconda-philippe

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

以下のshell sessionでは
(base) root@f19e2f06eabb:/#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。

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、どちらで捜査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)に気をつけてください。

##ファイル共有または複写
dockerとdockerを起動したOSでは、ファイル共有をするか、ファイル複写するかして、生成したファイルをブラウザ等表示させてください。参考文献欄にやり方のURLを記載しています。

複写の場合は、dockerを起動したOS側コマンドを実行しました。お使いのdockerの番号で置き換えてください。複写したファイルをブラウザで表示し内容確認しました。

plt.show()
はコメントにしている。

import matplotlib as mpl
mpl.use('Agg')
fig = plt.figure()
fig.savefig('img.png')
４行を追加している。ただし、ファイルが2kで中身不明。

Chapter 01

(base) root@b350954ba6b4:/# ls
DLwithPyTorch				 boot			      home					   mnt				run   usr
Practical-Convolutional-Neural-Networks  deep-learning-with-keras-ja  lib					   opt				sbin  var
Python-Deep-Learning			 dev			      lib64					   proc				srv
advanced-deep-learning-keras		 etc			      machine-learning-with-python-cookbook-notes  pytorch-nlp-tutorial-eu2018	sys
bin					 feature-engineering-book     media					   root				tmp
(base) root@b350954ba6b4:/# cd advanced-deep-learning-keras/
(base) root@b350954ba6b4:/advanced-deep-learning-keras# ls
README.md  s1  s2  s3  s4  s5  s6  s7
(base) root@b350954ba6b4:/advanced-deep-learning-keras# cd s1
(base) root@b350954ba6b4:/advanced-deep-learning-keras/s1# ls
1.2  1.3  1.4
(base) root@b350954ba6b4:/advanced-deep-learning-keras/s1# cd 1.2
(base) root@b350954ba6b4:/advanced-deep-learning-keras/s1/1.2# ls
1_linear_regression.py	2_cost_function.py  3_underfitting_overfitting.py  4_hyper_parameters.py
(base) root@b350954ba6b4:/advanced-deep-learning-keras/s1/1.2# python 1_linear_regression.py 

Using TensorFlow backend.
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense_1 (Dense)              (None, 1)                 2         
=================================================================
Total params: 2
Trainable params: 2
Non-trainable params: 0
_________________________________________________________________
Train on 128 samples, validate on 128 samples
Epoch 1/100
2018-10-22 10:06:24.431168: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA
2018-10-22 10:06:24.435316: I tensorflow/core/common_runtime/process_util.cc:69] Creating new thread pool with default inter op setting: 2. Tune using inter_op_parallelism_threads for best performance.
128/128 [==============================] - 0s 3ms/step - loss: 104.1362 - val_loss: 90.5734
Epoch 2/100
128/128 [==============================] - 0s 15us/step - loss: 92.5201 - val_loss: 80.6777
Epoch 3/100
128/128 [==============================] - 0s 16us/step - loss: 81.9747 - val_loss: 71.7360
Epoch 4/100
128/128 [==============================] - 0s 14us/step - loss: 72.4919 - val_loss: 63.6906
Epoch 5/100
128/128 [==============================] - 0s 15us/step - loss: 64.0199 - val_loss: 56.4262
Epoch 6/100
128/128 [==============================] - 0s 16us/step - loss: 56.4457 - val_loss: 49.7797
Epoch 7/100
128/128 [==============================] - 0s 19us/step - loss: 49.6001 - val_loss: 43.5884
Epoch 8/100
128/128 [==============================] - 0s 17us/step - loss: 43.3045 - val_loss: 37.7491
Epoch 9/100
128/128 [==============================] - 0s 13us/step - loss: 37.4325 - val_loss: 32.2385
Epoch 10/100
128/128 [==============================] - 0s 15us/step - loss: 31.9378 - val_loss: 27.0911
Epoch 11/100
128/128 [==============================] - 0s 13us/step - loss: 26.8356 - val_loss: 22.3654
Epoch 12/100
128/128 [==============================] - 0s 13us/step - loss: 22.1704 - val_loss: 18.1185
Epoch 13/100
128/128 [==============================] - 0s 39us/step - loss: 17.9898 - val_loss: 14.3914
Epoch 14/100
128/128 [==============================] - 0s 16us/step - loss: 14.3275 - val_loss: 11.2009
Epoch 15/100
128/128 [==============================] - 0s 28us/step - loss: 11.1955 - val_loss: 8.5368
Epoch 16/100
128/128 [==============================] - 0s 12us/step - loss: 8.5796 - val_loss: 6.3636
Epoch 17/100
128/128 [==============================] - 0s 15us/step - loss: 6.4412 - val_loss: 4.6260
Epoch 18/100
128/128 [==============================] - 0s 13us/step - loss: 4.7238 - val_loss: 3.2594
Epoch 19/100
128/128 [==============================] - 0s 14us/step - loss: 3.3627 - val_loss: 2.2007
Epoch 20/100
128/128 [==============================] - 0s 13us/step - loss: 2.2967 - val_loss: 1.3961
Epoch 21/100
128/128 [==============================] - 0s 14us/step - loss: 1.4755 - val_loss: 0.8049
Epoch 22/100
128/128 [==============================] - 0s 16us/step - loss: 0.8626 - val_loss: 0.3979
Epoch 23/100
128/128 [==============================] - 0s 14us/step - loss: 0.4333 - val_loss: 0.1532
Epoch 24/100
128/128 [==============================] - 0s 14us/step - loss: 0.1697 - val_loss: 0.0522
Epoch 25/100
128/128 [==============================] - 0s 16us/step - loss: 0.0560 - val_loss: 0.0746
Epoch 26/100
128/128 [==============================] - 0s 22us/step - loss: 0.0731 - val_loss: 0.1966
Epoch 27/100
128/128 [==============================] - 0s 17us/step - loss: 0.1971 - val_loss: 0.3903
Epoch 28/100
128/128 [==============================] - 0s 22us/step - loss: 0.3984 - val_loss: 0.6251
Epoch 29/100
128/128 [==============================] - 0s 17us/step - loss: 0.6441 - val_loss: 0.8716
Epoch 30/100
128/128 [==============================] - 0s 15us/step - loss: 0.9017 - val_loss: 1.1042
Epoch 31/100
128/128 [==============================] - 0s 13us/step - loss: 1.1432 - val_loss: 1.3047
Epoch 32/100
128/128 [==============================] - 0s 13us/step - loss: 1.3488 - val_loss: 1.4628
Epoch 33/100
128/128 [==============================] - 0s 17us/step - loss: 1.5073 - val_loss: 1.5758
Epoch 34/100
128/128 [==============================] - 0s 35us/step - loss: 1.6162 - val_loss: 1.6459
Epoch 35/100
128/128 [==============================] - 0s 15us/step - loss: 1.6789 - val_loss: 1.6780
Epoch 36/100
128/128 [==============================] - 0s 18us/step - loss: 1.7017 - val_loss: 1.6772
Epoch 37/100
128/128 [==============================] - 0s 14us/step - loss: 1.6911 - val_loss: 1.6472
Epoch 38/100
128/128 [==============================] - 0s 15us/step - loss: 1.6521 - val_loss: 1.5899
Epoch 39/100
128/128 [==============================] - 0s 29us/step - loss: 1.5875 - val_loss: 1.5064
Epoch 40/100
128/128 [==============================] - 0s 30us/step - loss: 1.4989 - val_loss: 1.3980
Epoch 41/100
128/128 [==============================] - 0s 15us/step - loss: 1.3877 - val_loss: 1.2676
Epoch 42/100
128/128 [==============================] - 0s 18us/step - loss: 1.2565 - val_loss: 1.1205
Epoch 43/100
128/128 [==============================] - 0s 33us/step - loss: 1.1101 - val_loss: 0.9638
Epoch 44/100
128/128 [==============================] - 0s 16us/step - loss: 0.9554 - val_loss: 0.8059
Epoch 45/100
128/128 [==============================] - 0s 16us/step - loss: 0.8001 - val_loss: 0.6548
Epoch 46/100
128/128 [==============================] - 0s 15us/step - loss: 0.6520 - val_loss: 0.5170
Epoch 47/100
128/128 [==============================] - 0s 15us/step - loss: 0.5170 - val_loss: 0.3966
Epoch 48/100
128/128 [==============================] - 0s 16us/step - loss: 0.3990 - val_loss: 0.2952
Epoch 49/100
128/128 [==============================] - 0s 15us/step - loss: 0.2992 - val_loss: 0.2121
Epoch 50/100
128/128 [==============================] - 0s 15us/step - loss: 0.2170 - val_loss: 0.1455
Epoch 51/100
128/128 [==============================] - 0s 93us/step - loss: 0.1505 - val_loss: 0.0935
Epoch 52/100
128/128 [==============================] - 0s 39us/step - loss: 0.0978 - val_loss: 0.0542
Epoch 53/100
128/128 [==============================] - 0s 12us/step - loss: 0.0574 - val_loss: 0.0263
Epoch 54/100
128/128 [==============================] - 0s 35us/step - loss: 0.0282 - val_loss: 0.0091
Epoch 55/100
128/128 [==============================] - 0s 44us/step - loss: 0.0099 - val_loss: 0.0015
Epoch 56/100
128/128 [==============================] - 0s 34us/step - loss: 0.0016 - val_loss: 0.0023
Epoch 57/100
128/128 [==============================] - 0s 17us/step - loss: 0.0023 - val_loss: 0.0098
Epoch 58/100
128/128 [==============================] - 0s 20us/step - loss: 0.0100 - val_loss: 0.0216
Epoch 59/100
128/128 [==============================] - 0s 38us/step - loss: 0.0224 - val_loss: 0.0355
Epoch 60/100
128/128 [==============================] - 0s 28us/step - loss: 0.0369 - val_loss: 0.0493
Epoch 61/100
128/128 [==============================] - 0s 24us/step - loss: 0.0512 - val_loss: 0.0615
Epoch 62/100
128/128 [==============================] - 0s 26us/step - loss: 0.0636 - val_loss: 0.0713
Epoch 63/100
128/128 [==============================] - 0s 14us/step - loss: 0.0734 - val_loss: 0.0785
Epoch 64/100
128/128 [==============================] - 0s 16us/step - loss: 0.0802 - val_loss: 0.0832
Epoch 65/100
128/128 [==============================] - 0s 15us/step - loss: 0.0844 - val_loss: 0.0857
Epoch 66/100
128/128 [==============================] - 0s 15us/step - loss: 0.0863 - val_loss: 0.0860
Epoch 67/100
128/128 [==============================] - 0s 14us/step - loss: 0.0861 - val_loss: 0.0842
Epoch 68/100
128/128 [==============================] - 0s 16us/step - loss: 0.0839 - val_loss: 0.0802
Epoch 69/100
128/128 [==============================] - 0s 41us/step - loss: 0.0797 - val_loss: 0.0741
Epoch 70/100
128/128 [==============================] - 0s 22us/step - loss: 0.0735 - val_loss: 0.0663
Epoch 71/100
128/128 [==============================] - 0s 15us/step - loss: 0.0657 - val_loss: 0.0573
Epoch 72/100
128/128 [==============================] - 0s 15us/step - loss: 0.0568 - val_loss: 0.0477
Epoch 73/100
128/128 [==============================] - 0s 19us/step - loss: 0.0474 - val_loss: 0.0383
Epoch 74/100
128/128 [==============================] - 0s 35us/step - loss: 0.0382 - val_loss: 0.0297
Epoch 75/100
128/128 [==============================] - 0s 18us/step - loss: 0.0298 - val_loss: 0.0222
Epoch 76/100
128/128 [==============================] - 0s 28us/step - loss: 0.0224 - val_loss: 0.0160
Epoch 77/100
128/128 [==============================] - 0s 19us/step - loss: 0.0163 - val_loss: 0.0109
Epoch 78/100
128/128 [==============================] - 0s 48us/step - loss: 0.0113 - val_loss: 0.0070
Epoch 79/100
128/128 [==============================] - 0s 16us/step - loss: 0.0073 - val_loss: 0.0040
Epoch 80/100
128/128 [==============================] - 0s 15us/step - loss: 0.0042 - val_loss: 0.0019
Epoch 81/100
128/128 [==============================] - 0s 13us/step - loss: 0.0020 - val_loss: 5.7623e-04
Epoch 82/100
128/128 [==============================] - 0s 19us/step - loss: 6.2660e-04 - val_loss: 6.0980e-05
Epoch 83/100
128/128 [==============================] - 0s 41us/step - loss: 6.6936e-05 - val_loss: 2.1531e-04
Epoch 84/100
128/128 [==============================] - 0s 15us/step - loss: 2.1649e-04 - val_loss: 8.7550e-04
Epoch 85/100
128/128 [==============================] - 0s 15us/step - loss: 9.0487e-04 - val_loss: 0.0018
Epoch 86/100
128/128 [==============================] - 0s 14us/step - loss: 0.0019 - val_loss: 0.0029
Epoch 87/100
128/128 [==============================] - 0s 26us/step - loss: 0.0030 - val_loss: 0.0039
Epoch 88/100
128/128 [==============================] - 0s 17us/step - loss: 0.0040 - val_loss: 0.0047
Epoch 89/100
128/128 [==============================] - 0s 28us/step - loss: 0.0049 - val_loss: 0.0054
Epoch 90/100
128/128 [==============================] - 0s 19us/step - loss: 0.0055 - val_loss: 0.0058
Epoch 91/100
128/128 [==============================] - 0s 14us/step - loss: 0.0059 - val_loss: 0.0060
Epoch 92/100
128/128 [==============================] - 0s 13us/step - loss: 0.0061 - val_loss: 0.0061
Epoch 93/100
128/128 [==============================] - 0s 16us/step - loss: 0.0061 - val_loss: 0.0060
Epoch 94/100
128/128 [==============================] - 0s 46us/step - loss: 0.0059 - val_loss: 0.0056
Epoch 95/100
128/128 [==============================] - 0s 16us/step - loss: 0.0056 - val_loss: 0.0052
Epoch 96/100
128/128 [==============================] - 0s 14us/step - loss: 0.0051 - val_loss: 0.0045
Epoch 97/100
128/128 [==============================] - 0s 14us/step - loss: 0.0045 - val_loss: 0.0038
Epoch 98/100
128/128 [==============================] - 0s 13us/step - loss: 0.0038 - val_loss: 0.0030
Epoch 99/100
128/128 [==============================] - 0s 17us/step - loss: 0.0030 - val_loss: 0.0023
Epoch 100/100
128/128 [==============================] - 0s 15us/step - loss: 0.0023 - val_loss: 0.0017
[array([[3.003418]], dtype=float32), array([10.041711], dtype=float32)]

##1.2

(base) root@b350954ba6b4:/advanced-deep-learning-keras/s1/1.2# python 2_cost_function.py 
targets             = [1 2 3]
predictions         = [0 1 8]
Regression cost =  9.0
targets              = [0 1 1]
good predictions     = [0.1 0.9 0.9]
bad predictions      = [0.1 0.9 0.9]
Classification cost (good) =  0.07024034377188419
Classification cost (bad)  =  1.3040076684760484

3_underfitting_overfitting.py

(base) root@b350954ba6b4:/advanced-deep-learning-keras/s1/1.2# python 3_underfitting_overfitting.py 
None
/opt/conda/lib/python3.6/site-packages/matplotlib/figure.py:448: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.
  % get_backend())

edit 3_underfitting_overfitting.py

(base) root@b350954ba6b4:/advanced-deep-learning-keras/s1/1.2# vi 3_underfitting_overfitting.py 
(base) root@b350954ba6b4:/advanced-deep-learning-keras/s1/1.2# python 3_underfitting_overfitting.py 
None
(base) root@b350954ba6b4:/advanced-deep-learning-keras/s1/1.2# ls
1_linear_regression.py	2_cost_function.py  3_underfitting_overfitting.py  4_hyper_parameters.py  img.png

(base) root@b350954ba6b4:/advanced-deep-learning-keras/s1/1.2# python 4_hyper_parameters.py 
(442, 10)
(442,)
Score with default parameters =  0.4512313946799056
Score with Grid Search parameters = 0.48879020446060156 best alpha = 0.001
Score with Random Search parameters = 0.48905379594162485 best alpha = 0.04036024496265811

image_ocr.py

(base) root@b350954ba6b4:/advanced-deep-learning-keras/s2/1.1/keras/examples# python image_ocr.py 
Traceback (most recent call last):
  File "image_ocr.py", line 40, in <module>
    import cairocffi as cairo
  File "/opt/conda/lib/python3.6/site-packages/cairocffi/__init__.py", line 41, in <module>
    cairo = dlopen(ffi, 'cairo', 'cairo-2', 'cairo-gobject-2')
  File "/opt/conda/lib/python3.6/site-packages/cairocffi/__init__.py", line 38, in dlopen
    raise OSError("dlopen() failed to load a library: %s" % ' / '.join(names))
OSError: dlopen() failed to load a library: cairo / cairo-2 / cairo-gobject-2

(base) root@b350954ba6b4:/advanced-deep-learning-keras/s3/1.1# python img_classification_example.py 
Traceback (most recent call last):
  File "img_classification_example.py", line 7, in <module>
    import matplotlib.pyplot as plt
  File "/opt/conda/lib/python3.6/site-packages/matplotlib/pyplot.py", line 2371, in <module>
    switch_backend(rcParams["backend"])
  File "/opt/conda/lib/python3.6/site-packages/matplotlib/__init__.py", line 892, in __getitem__
    plt.switch_backend(rcsetup._auto_backend_sentinel)
  File "/opt/conda/lib/python3.6/site-packages/matplotlib/pyplot.py", line 196, in switch_backend
    switch_backend(candidate)
  File "/opt/conda/lib/python3.6/site-packages/matplotlib/pyplot.py", line 207, in switch_backend
    backend_mod = importlib.import_module(backend_name)
  File "/opt/conda/lib/python3.6/importlib/__init__.py", line 126, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "/opt/conda/lib/python3.6/site-packages/matplotlib/backends/backend_gtk3agg.py", line 6, in <module>
    from . import backend_agg, backend_cairo, backend_gtk3
  File "/opt/conda/lib/python3.6/site-packages/matplotlib/backends/backend_cairo.py", line 19, in <module>
    import cairocffi as cairo
  File "/opt/conda/lib/python3.6/site-packages/cairocffi/__init__.py", line 41, in <module>
    cairo = dlopen(ffi, 'cairo', 'cairo-2', 'cairo-gobject-2')
  File "/opt/conda/lib/python3.6/site-packages/cairocffi/__init__.py", line 38, in dlopen
    raise OSError("dlopen() failed to load a library: %s" % ' / '.join(names))
OSError: dlopen() failed to load a library: cairo / cairo-2 / cairo-gobject-2
``
## jupyter notebook

jupyter notebook --ip=0.0.0.0 --allow-root


<img width="994" alt="py30-1.png" src="https://qiita-image-store.s3.amazonaws.com/0/51423/f6e0fb31-6c23-fb6c-bdc4-6fc2a572746c.png">

<img width="980" alt="py30-2.png" src="https://qiita-image-store.s3.amazonaws.com/0/51423/6b9fefa1-6033-111b-d0b5-de7f0d5bfbea.png">

![py30-3.png](https://qiita-image-store.s3.amazonaws.com/0/51423/d29e15e3-ca1b-ace2-28b6-ac2fbb806b2e.png)

<img width="978" alt="py30-4.png" src="https://qiita-image-store.s3.amazonaws.com/0/51423/448ecc02-c1c3-9203-ff36-eda781c3d45d.png">

<img width="971" alt="py30-5.png" src="https://qiita-image-store.s3.amazonaws.com/0/51423/8aeb7a2d-0248-71c3-3c5c-7f885c87b4a2.png">



# 2. dockerを自力で構築する方へ

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。本の続きを実行する上では必要ありません。
自力でdocker/anaconda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 kaizenjapan/anaconda-keras
Using default tag: latest
latest: Pulling from continuumio/anaconda3
Digest: sha256:e07b9ca98ac1eeb1179dbf0e0bbcebd87701f8654878d6d8ce164d71746964d1
Status: Image is up to date for continuumio/anaconda3:latest

$ docker run -it -p 8888:8888 continuumio/anaconda3 /bin/bash

実際にはkeras, tensorflow を利用していた他のpushをpull

## apt

```shell-session
(base) root@d8857ae56e69:/# apt update; apt -y upgrade

(base) root@d8857ae56e69:/# apt install -y procps vim apt-utils sudo
```

## ソース git

```
(base) root@f19e2f06eabb:/# git clone https://github.com/philipperemy/advanced-deep-learning-keras
```

## conda

```
# conda update --prefix /opt/conda anaconda
Solving environment: done

# conda install theano
```

## pip

```
(base) root@f19e2f06eabb:/deep-learning-from-scratch-2/ch01# 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)
    100% |████████████████████████████████| 1.3MB 2.0MB/s 
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
(
```

# docker hubへの登録

```
$ docker ps
CONTAINER ID        IMAGE                   COMMAND                  CREATED             STATUS              PORTS                    NAMES
caef766a99ff        continuumio/anaconda3   "/usr/bin/tini -- /b…"   10 hours ago        Up 10 hours         0.0.0.0:8888->8888/tcp   sleepy_bassi

$ docker commit caef766a99ff kaizenjapan/anaconda-philippe

$ docker push kaizenjapan/anaconda-philippe
```

# 参考資料(reference)

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

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

OpenCVをPythonで動かそうとしてlibGL.soが無いって言われたけど解決した。
https://qiita.com/toshitanian/items/5da24c0c0bd473d514c8

サーバサイドにおけるmatplotlibによる作図Tips
https://qiita.com/TomokIshii/items/3a26ee4453f535a69e9e

Dockerでホストとコンテナ間でのファイルコピー
https://qiita.com/gologo13/items/7e4e404af80377b48fd5

Docker for Mac でファイル共有を利用する
https://qiita.com/seijimomoto/items/1992d68de8baa7e29bb5


「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


Ubuntu 16.04 LTS で NVIDIA Docker を使ってみる
https://blog.amedama.jp/entry/2017/04/03/235901

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

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

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

Autosar Guidelines C++14 example code compile list(1-169)
https://qiita.com/kaizen_nagoya/items/8ccbf6675c3494d57a76

Error一覧(C/C++, python, bash...) Error(0)
https://qiita.com/kaizen_nagoya/items/48b6cbc8d68eae2c42b8

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

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

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

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

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
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