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参考文献の参考文献の参考文献は、自然言語処理の対象として、読書会の最後までには作成予定。



<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 in order.
# 参考文献
なお、下記は最新のURLなど、原著とはすでに違う状態になっています。原著の情報は
「ゼロから作るDeepLearning2 自然言語処理編」読書会用資料を　ゼロから作る。現在参考文献確認中。
https://qiita.com/kaizen_nagoya/items/33fb2c66175a25e39559
をご覧ください。このQiita資料も誤植を含んでいるかもしれません。コメント等でご指摘くださると幸いです。

## python関連
1 Broadcasting 
https://docs.scipy.org/doc/numpy-1.15.0/reference/ufuncs.html#broadcasting
参考文献見当たらず。

2.100 numpy exercises
https://github.com/rougier/numpy-100
参考文献見当たらず

3. Cupy web page
 https://cupy.chainer.org/
参考文献見当たらず。「文献４は３の中」

4.Cupy install page
http://docs-cupy.chainer.org/en/stable/install.html
参考文献見当たらず

##ディプラーニングの基本事項
5.斎藤康毅, 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, オライリー, 2016, ISBN978-4-87311-758-4
https://www.oreilly.co.jp/books/9784873117584/

「ゼロから作るDeep Learning」参考文献一覧
https://qiita.com/kaizen_nagoya/items/82975f7b63b6ea2f33ff
https://researchmap.jp/joxn1ul6v-2078500/#_2078500

6.Gupta. Suyog. et al: "Deep learning with limited numerical precision.", Proceedings of the 32nd International Conference on Machine Learning (ICML-15) 2015
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.1001.5463&rep=rep1&type=pdf

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7.Jouppi. Norman P.. et al: "In-datacenter performance analysis of a tensor processing unit."Proceedings of the 44th Annual International Symposium on Computer Architecture, ACM. 2017
https://arxiv.org/abs/1704.04760

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Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. ICLR, 2015.
Ryan Kiros, Ruslan Salakhutdinov, and Richard S Zemel. Unifying visual-semantic embeddings with multi- modal neural language models. arXiv preprint arXiv:1411.2539, 2014.
D. Kingma and J. L. Ba. Adam: a method for stochastic optimization. ICLR, 2014. arXiv:1412.6980.
Liwei Wang, Yin Li, and Svetlana Lazebnik. Learning deep structure-preserving image-text embeddings.
CVPR, 2016.
Karl Moritz Hermann, Tomas Kocisky, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman,
and Phil Blunsom. Teaching machines to read and comprehend. In NIPS, 2015.
Ryan Kiros, Yukun Zhu, Ruslan R Salakhutdinov, Richard Zemel, Raquel Urtasun, Antonio Torralba, and Sanja
Fidler. Skip-thought vectors. In NIPS, 2015.
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. Efficient estimation of word representations in
vector space. arXiv preprint arXiv:1301.3781, 2013.
Yukun Zhu, Ryan Kiros, Rich Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, and Sanja Fidler. Aligning books and movies: Towards story-like visual explanations by watching movies and reading books. In ICCV, 2015.
Marco Marelli, Luisa Bentivogli, Marco Baroni, Raffaella Bernardi, Stefano Menini, and Roberto Zamparelli. Semeval-2014 task 1: Evaluation of compositional distributional semantic models on full sentences through semantic relatedness and textual entailment. SemEval-2014, 2014.
Bo Pang and Lillian Lee. Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. In ACL, pages 115–124, 2005.
Minqing Hu and Bing Liu. Mining and summarizing customer reviews. In Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, 2004.
Bo Pang and Lillian Lee. A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In ACL, 2004.
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Hugo Larochelle and Iain Murray. The neural autoregressive distribution estimator. In AISTATS, volume 6, page 622, 2011.
Marcus Liwicki and Horst Bunke. Iam-ondb-an on-line english sentence database acquired from handwritten text on a whiteboard. In ICDAR, 2005.
Alex Graves. Generating sequences with recurrent neural networks. arXiv preprint arXiv:1308.0850, 2013.

9.Nitish Srivastava, et al: "Dropout: A Simple Way to Prevent Neural Networks from Overfitting."Journal of Machine Learning Research 15, 2014, p.1929-1958
http://www.cs.toronto.edu/%7Ersalakhu/papers/srivastava14a.pdf

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##ディープラーニングによる自然言語処理
10.Stanford University CS224d: DeepLearning for Natural Language Processing
http://cs224d.stanford.edu

Intro to NLP and Deep Learning 
Suggested Readings:
[Linear Algebra Review]
[Probability Review]
[Convex Optimization Review]
[More Optimization (SGD) Review]
[From Frequency to Meaning: Vector Space Models of Semantics]

Simple Word Vector representations: word2vec, GloVe
Suggested Readings:
[Distributed Representations of Words and Phrases and their Compositionality]
[Efficient Estimation of Word Representations in Vector Space]

Advanced word vector representations: language models, softmax, single layer networks
Suggested Readings:
[GloVe: Global Vectors for Word Representation]
[Improving Word Representations via Global Context and Multiple Word Prototypes]

（続く）


11. Oxford Deep NLP 2017 course
http://github.com/oxford-cs-deepnlp-2017/lectures

3. Lecture 2a- Word Level Semantics [Ed Grefenstette]

Words are the core meaning bearing units in language. Representing and learning the meanings of words is a fundamental task in NLP and in this lecture the concept of a word embedding is introduced as a practical and scalable solution.

[slides] [video]

Reading

Embeddings Basics

Firth, John R. "A synopsis of linguistic theory, 1930-1955." (1957): 1-32.
Curran, James Richard. "From distributional to semantic similarity." (2004).
Collobert, Ronan, et al. "Natural language processing (almost) from scratch." Journal of Machine Learning Research 12. Aug (2011): 2493-2537.
Mikolov, Tomas, et al. "Distributed representations of words and phrases and their compositionality." Advances in neural information processing systems. 2013.
Datasets and Visualisation

Finkelstein, Lev, et al. "Placing search in context: The concept revisited." Proceedings of the 10th international conference on World Wide Web. ACM, 2001.
Hill, Felix, Roi Reichart, and Anna Korhonen. "Simlex-999: Evaluating semantic models with (genuine) similarity estimation." Computational Linguistics (2016).
Maaten, Laurens van der, and Geoffrey Hinton. "Visualizing data using t-SNE." Journal of Machine Learning Research 9.Nov (2008): 2579-2605.
Blog posts

Deep Learning, NLP, and Representations, Christopher Olah.
Visualizing Top Tweeps with t-SNE, in Javascript, Andrej Karpathy.
Further Reading

Hermann, Karl Moritz, and Phil Blunsom. "Multilingual models for compositional distributed semantics." arXiv preprint arXiv:1404.4641 (2014).
Levy, Omer, and Yoav Goldberg. "Neural word embedding as implicit matrix factorization." Advances in neural information processing systems. 2014.
Levy, Omer, Yoav Goldberg, and Ido Dagan. "Improving distributional similarity with lessons learned from word embeddings." Transactions of the Association for Computational Linguistics 3 (2015): 211-225.
Ling, Wang, et al. "Two/Too Simple Adaptations of Word2Vec for Syntax Problems." HLT-NAACL. 2015.jkhんjkl；：」

12. Young D. Hazarika, S. Peoria. and E. Cambria: ""Recent trends in deep learning based natural language processing." in arXiv preprint  arXiv:1708.02709, 2017
https://arxiv.org/abs/1708.02709
13. 坪井裕太,海野裕也, 鈴木潤:「深層学習による自然言語処理(機械学習プロフェッショナルシリーズ)」講談社, 2017, ISBN 978-4-06-152924-3
https://www.kspub.co.jp/book/detail/1529243.html

## ディープラーニング登場以前の自然言語処理
14.Steven Bird. Iwane Klein, Edward Loper:「入門　自然言語処理」, オライリージャパン, 2010,ISBN978-4-87311-470-5
https://www.oreilly.co.jp/books/9784873114705/
15.Jeffrey E.F. Friedl:「詳説　正規表現第3版」オライリージャパン, 2008, ISBN978-4-87311-359-3
https://www.oreilly.co.jp/books/9784873113593/
16.Christopher D. Manning, Hinrich Schutze:「統計的自然言語処理の基礎」共立出版, 2017, ISBN 978-4-320-12421-9 
http://www.kyoritsu-pub.co.jp/bookdetail/9784320124219
17.Miller, George A;"Wordnet: a lexical database for English.", Communications of the  ACM 38.11, 1995, p.39-41
http://l2r.cs.uiuc.edu/~danr/Teaching/CS598-05/Papers/miller95.pdf
18.WordNet Interface
http://www.nltk.org/howto/wordnet.html

## カウントベース手法による単語ベクトル
19.Church, Kenneth Ward, and Patrick Hanks: "Word association norms, mutual information, and lexicography.", Communicational linguistics 16.1, 1990, p.22-29 
http://www.aclweb.org/anthology/J90-1003
20. Deerwester, Scott, et al:"Indexing by latent semantic analysis.", Journal of the american society for information science 41.6, 1990, p.391-407
http://www.cs.bham.ac.uk/~pxt/IDA/lsa_ind.pdf
第一刷には終了ページの記載が抜けている。
21. TruncatedSVD
http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.TruncatedSVD.html

## word2vec関連
22.Mikolov, Tomas, et al:"Efficient estimation of word representations in vector space." arXiv preprint arXiv:1301.3781, 2013
https://arxiv.org/abs/1301.3781
23.Mikolov, Tomas, et al:"Distributed representations of words and phrases and their compositionally.", Advances in neural information processing systems, 2013
https://arxiv.org/pdf/1310.4546.pdf
24.Baroni, Marco, Georgiana Dinu, and Germán Kruszewski:"Don't count, predict! A systematic comparison of context-counting vs. context-predicting semantic vectors.", ACL(1), 2014, 
雑誌の記述が不明。ACLはAssociation for Computational Linguistics.の略。雑誌名はProceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, p.238–247
http://www.aclweb.org/anthology/P14-1023
25.Levy, Omer, Yoan Goldberg, and Ido Dagan: "Improving distributional similarity with lessons learned from word embeddings." Transactions of the Association for Computational Linguistics 3, 2015, p.211-225.
https://levyomer.files.wordpress.com/2015/03/improving-distributional-similarity-tacl-2015.pdf
26.Levy, Omer, Yoan Goldberg:"Neural word embedding as implicit matrix factorization." Advances in neural information processing systems, 2014
https://papers.nips.cc/paper/5477-neural-word-embedding-as-implicit-matrix-factorization.pdf
27. Pennington, Jeffrey, Richard Soccer, and Christopher D. Manning:"Glove: Global Vectors for Word Representation.", EMNLP. VOl14. 2014
https://nlp.stanford.edu/pubs/glove.pdf
表題"Glove:"ではなく、"GloVe:"
28.Bengio, Yoshua, et al."A neural probabilistic language model.", Journal of machine learning research 3. Feb, 2003, p.1137-1155.
http://www.iro.umontreal.ca/~vincentp/Publications/lm_jmlr.pdf
## RNN関連
29.Talathi, Sachin S., and Aniket Vartan:"Improving performance of recurrent neural network with rely nonlinearity.", arXiv preprint arXiv:1511.03771, 2015
https://arxiv.org/abs/1511.03771
30.Pascanu, Razan, Tomas Mikolov, and Yoshua Bengio:"On the difficulty of training recurrent neural networks.", International Conference on Machine Learning, 2013
http://proceedings.mlr.press/v28/pascanu13.pdf
31.colah's blog:"Understanding LSTM Networks",2015, 
http://colah.github.io/posts/2015-08-Understanding-LSTMs/
32.Chung, Junyoung, et al:"Empirical evaluation of gated recurrent neural networks on sequence modeling." arXiv preprint arXiv:1412.3355, 2014
https://arxiv.org/abs/1412.3555
33. Jozefowicz, Rafal, Wojciech Zaremba, and Ilya Sutskever:"An empirical exploration of recurrent network architectures." International Conference on Machine Learning, 2015
http://proceedings.mlr.press/v37/jozefowicz15.pdf
誤植：Jozefowicz, Rafal, Wojciech Zaremba, and Ilya Sutskever
原情報：Rafal Jozefowicz , Wojciech Zaremba , Ilya Sutskever

# RNNによる言語モデル
34.Merity, Stephen, Nitish Shirish Keskar, and Richard Socher:"Regularizing and optimizing LSTM language models." arXiv preprint arXiv:1708.02182, 2017
誤植：Merity, Stephen, Nitish Shirish Keskar, and Richard Socher
原情報：Stephen Merity, Nitish Shirish Keskar, Richard Socher
https://arxiv.org/abs/1708.02182
35.Zaremba, Wojciech, IIya Sutskever, and Oriol Vinyals:"Recurrent neural netwok regularization." arXiv preprint arXiv:1409.2329, 2014,
誤植：Zaremba, Wojciech, IIya Sutskever, and Oriol Vinyals
原情報：Wojciech Zaremba, Ilya Sutskever, Oriol Vinyals
36.Gal, Yarin, and Zoubin Ghahramani:"A theoretically grounded application of dropout in recurrent neural networks.", Advances in neural information processing systems, 2016
https://arxiv.org/abs/1512.05287
誤植：Gal, Yarin, and Zoubin Ghahramani
原情報：Yarin Gal, Zoubin Ghahramani
37.Press, Ofir, and Lior Wolf:"Using the output embedding to improve language models." arXiv preprint arXiv:1608.05859, 2016
https://arxiv.org/abs/1608.05859
誤植：Press, Ofir, and Lior Wolf
原情報：Ofir Press, Lior Wolf
38 Inan, Hakan, Khashayar Khosravi, and Richard Socher:"Tying Word Vectors and Word Classifiers: A Loss Framework for Language Modeling." arXiv preprint arXiv:1611.01462, 2016
https://arxiv.org/abs/1611.01462
「誤植：Inan, Hakan, Khashayar Khosravi, and Richard Socher
原情報：Hakan Inan, Khashayar Khosravi, Richard Socher」
39. PyTorch Examples, "Word-level language modeling RNN"
http://github.com/pytorchexamples/tree/0.3/word_language_model
「0.4が出ている。またmasterはこちらでo.3は”This branch is 15 commits behind master.”。
https://github.com/pytorch/examples/tree/master/word_language_model」
## seqwseq関連
40.Keras examples, "Implementation of sequence to sequence learning for performing addition of two numbers (as string)"
https://github.com/keras-team/keras/blob/2.0.0/examples/addtion_rnn.py
「2.0.0はNot found
https://github.com/keras-team/keras/blob/master/examples/addition_rnn.py」
41.Sutskever, Ilya, Oriol Vinyals, and Quoc V. Le:"Sequence to sequence learning with neural networks.", Advances in neural information processing systems. 2014.
「誤植：Sutskever, Ilya, Oriol Vinyals, and Quoc V. Le
原情報： Ilya Sutskever , Oriol Vinyals , Quoc V. Le」
42.Cho, Kyunghyun, et al:"Learning phrase representations using RNN encoder-decoder for statistical machine translation.", arXiv preprint arXiv:1406.1078
https://arxiv.org/abs/1406.1078
「誤植：Cho, Kyunghyun,
原情報：Kyunghyun Cho,」
43. Vinyals, Oriol, and Quoc Le:"A neural conversational model.", arXiv preprint arXiv:1506.05869, 2015
https://arxiv.org/abs/1506.05869
「誤植：Vinyals, Oriol, and Quoc Le
原情報：Oriol Vinyals, Quoc Le」
44.Zaremba, Wojciech, and Ilya Sutskever:"Learning to execute.", arXiv preprint arXiv:1410.4615, 2014
「誤植；Zaremba, Wojciech, and Ilya Sutskever
原情報；Wojciech Zaremba, Ilya Sutskever」
45. Vinyl, Oriol, et al:"Show and tell: A neural image caption generator.", Computer Vision and Pattern Recognition(CVPR), 2015 IEEE Conference on. IEEE, 2015
https://arxiv.org/pdf/1411.4555.pdf
「誤植；Vinyl, Oriol,
原情報；Oriol Vinyals」
46. Karpathy, Andrej and Li Fei-Fei:"Deep visual-semantic alignments for generating image descriptions." Proceedings of the IEEE conference on computer vision and pattern recognition. 2015
https://cs.stanford.edu/people/karpathy/cvpr2015.pdf
https://arxiv.org/abs/1412.2306
「誤植：Karpathy, Andrej and Li Fei-Fei
原情報：Andrej Karpathy , Li Fei-Fei」
47. Show and Tell: A neural Image Caption Generator
https://github.com/tensorflow/models/tree/master/research/im2txt
## Attention関連
48. Bahdanau, Dzmitry, Kyunghyun Cho, and Yoshua Bengio:"Neural machine translation by jointly learning to align and translate.", arXiv preprint arXiv:1409.0473, 2014
https://arxiv.org/abs/1409.0473
Bahdanau, Dzmitry, Kyunghyun Cho, and Yoshua Bengio
Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio
49.Luong, Minh-Thang, Hieu Pham, and Christopher D. Manning:"Effective approaches to attention-based neural machine translation.", arXiv prelprint arXiv:1508.04025, 2016
「誤植:prelprint
正:preprint
誤植：Luong, Minh-Thang, Hieu Pham, and Christopher D. Manning
原情報：Minh-Thang Luong, Hieu Pham, Christopher D. Manning」
50. Wu, Yonghui, et al:"Google's neural machine translation system: Bridging the gap between human and machine translation.", arXiv preprint arXiv:1609.08144, 2016
「誤植：Wu, Yonghui
原情報：Yonghui Wu」
51.Google Research Blog.
https://research.googleblog.com/2016/09/a-nurral-network-for-machine.html
「誤植：https://research.googleblog.com/2016/09/a-nurral-network-for-machine.html
原情報：https://ai.googleblog.com/2016/09/a-neural-network-for-machine.html」
たぶん、「Google Research Blog. Neural Network for Machine Translation, at Production Scale, https://research.googleblog.com/2016/09/」としておけば、接続が切れなかったと思われる。後の祭り。
52.Vaswani, Ashish, et al:"Attention Is All You Need.", arXiv preprint arXiv:1706.03762, 2017
https://arxiv.org/abs/1706.03762
「誤植：Vaswani, Ashish
原情報Ashish Vaswani」
53.Google Research Blog.
https://research.googleblog.com/2017/08/transformer-novel-neural-network.html
「誤植：https://research.googleblog.com/2017/08/transformer-novel-neural-network.html
原情報：https://ai.googleblog.com/2017/08/transformer-novel-neural-network.html」
たぶん、「Transformer: A Novel Neural Network Architecture for Language Understanding, Thursday, August 31, 2017, https://research.googleblog.com/2017/08/」としてあれば変更は必要なかったかも。
54.Gehring, Jones, et al:"Convolutional Sequence to Sequence Learning.", arXiv preprint arXiv:1705.03122, 2017
https://arxiv.org/abs/1705.03122
「誤植：Gehring, Jones,
原情報：Jonas Gehring」
##外部メモリ付きRNN
55.Graves, Alex, Greg Wayne, and Ivo Danihelka:"Neural Turing Machines.", arXiv preprint arXiv:1410.5401, 2014
https://arxiv.org/abs/1410.5401
「誤植：Graves, Alex, Greg Wayne, and Ivo Danihelka
原情報Alex Graves, Greg Wayne, Ivo Danihelka」
56.Graves, Alex, et al:"Hybrid computing using a neural network with dynamic external memory.", Nature 538.7626, 206, p471
abstract: https://www.nature.com/articles/nature20101
57. DeepMind Blog:"Differentiable neural computers",
https://deepmind.com/blog/differentiable-neural-computers/

# 関連資料
私が効果を確認した「小川メソッド」
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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

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

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. 0.01 初稿 20180805 午前
ver. 0.02 追記 20180805 午後
ver. 0.03 補足 20190623
### 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.

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