Pytorch復習

# pytorch

pytorch foundation
https://pytorch.org

Pytorch Begginer Series
https://www.youtube.com/playlist?list=PL_lsbAsL_o2CTlGHgMxNrKhzP97BaG9ZN

dockerで機械学習(23) with anaconda(23)「Deep Learning with PyTorch」By Vishnu Subramanian
https://qiita.com/kaizen_nagoya/items/77d6ee15edd7d6d06470

dockerで機械学習(26) with anaconda(26)「Natural Language Processing with PyTorch」By Delip Rao
https://qiita.com/kaizen_nagoya/items/db46e0f162fa07ec4a30

## Related items
### 1
色(11) いろいろ Countdown Calendar 2021 を満席にする5つの方法
https://qiita.com/kaizen_nagoya/items/8294b80c5fb8499298a9

@0sb1 PyTorchでRGBとHSVを微分可能な形で変換する
https://qiita.com/0sb1/items/20819a90872275811439

pytorch関数

### 2
「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

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」

### 3
深層学習五十論文 by 綿岡晃輝 単語帳 shell, awk, 英語(25) docker(101)
https://qiita.com/kaizen_nagoya/items/670d4d332e07fd2e5fc2

PyTorch: An Imperative Style, High-Performance Deep Learning Library	
https://arxiv.org/pdf/1912.01703.pdf

References
[1] Yangqing "Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, and Trevor" Darrell. "caffe: Convolutional architecture for fast feature embedding". "arXiv preprint arXiv:1408.5093", "2014".
[2] Frank Seide and Amit Agarwal. Cntk: Microsoft’s open-source deep-learning toolkit. In Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’16, pages 2135–2135, New York, NY, USA, 2016. ACM.
[3] Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dandelion Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng. TensorFlow: Large- scale machine learning on heterogeneous systems, 2015. Software available from tensorflow.org.
[4] Theano Development Team. Theano: A Python framework for fast computation of mathematical expressions. arXiv e-prints, abs/1605.02688, May 2016.
[5] Seiya Tokui, Kenta Oono, Shohei Hido, and Justin Clayton. Chainer: a next-generation open source framework for deep learning. In Proceedings of Workshop on Machine Learning Systems (LearningSys) in The Twenty-ninth Annual Conference on Neural Information Processing Systems (NIPS), 2015.
[6] Ronan Collobert, Samy Bengio, and Johnny Mariéthoz. Torch: a modular machine learning software library. Technical report, Idiap, 2002.
[7] G. Neubig, C. Dyer, Y. Goldberg, A. Matthews, W. Ammar, A. Anastasopoulos, M. Balles- teros, D. Chiang, D. Clothiaux, T. Cohn, K. Duh, M. Faruqui, C. Gan, D. Garrette, Y. Ji, L. Kong, A. Kuncoro, G. Kumar, C. Malaviya, P. Michel, Y. Oda, M. Richardson, N. Saphra, S. Swayamdipta, and P. Yin. DyNet: The Dynamic Neural Network Toolkit. ArXiv e-prints, January 2017.
[8] Philip S. Abrams. An APL Machine. PhD thesis, Stanford University, 1970.
[9] The MathWorks, Inc., Natick, Massachusetts, United States. MATLAB and Statistics Toolbox.
[10] R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for
Statistical Computing, Vienna, Austria.
[11] Jeff Bezanson, Alan Edelman, Stefan Karpinski, and Viral B Shah. Julia: A fresh approach to numerical computing. SIAM review, 59(1):65–98, 2017.
[12] Travis Oliphant. NumPy: A guide to NumPy. USA: Trelgol Publishing, 2006. http://www.numpy.org/.
[13] Gaël Guennebaud, Benoît Jacob, et al. Eigen v3. http://eigen.tuxfamily.org, 2010.
[14] Y LeCun and L Bottou. Lush reference manual. Technical report, code available at http://lush.sourceforge.net, 2002.
[15] Atilim Gunes Baydin, Barak A. Pearlmutter, Alexey Andreyevich Radul, and Jeffrey Mark Siskind. Automatic differentiation in machine learning: A survey. J. Mach. Learn. Res., 18(1):5595–5637, January 2017.
[16] Dougal Maclaurin. Modeling, Inference and Optimization with Composable Differentiable Procedures. PhD thesis, Harvard University, April 2016.
[17] Matthew Johnson et. al. Jax. https://github.com/google/jax, 2018.
[18] Mike Innes et. al. Flux.jl. https://github.com/FluxML/Flux.jl, 2018.
[19] Eric Jones, Travis Oliphant, Pearu Peterson, et al. SciPy: Open source scientific tools for Python, 2001–. http://www.scipy.org/.
[20] Wes McKinney. Data structures for statistical computing in python. In Proceedings of the 9th Python in Science Conference, 51-56, 2010.
[21] Pierre Sermanet, Koray Kavukcuoglu, and Yann LeCun. Eblearn: Open-source energy-based learning in c++. In 2009 21st IEEE International Conference on Tools with Artificial Intelligence, pages 693–697. IEEE, 2009.
[22] Sharan Chetlur, Cliff Woolley, Philippe Vandermersch, Jonathan D. Cohen, John Tran, Bryan Catanzaro, and Evan Shelhamer. cudnn: Efficient primitives for deep learning. CoRR, abs/1410.0759, 2014.
[23] Andrew Lavin. maxdnn: An efficient convolution kernel for deep learning with maxwell gpus, January 2015.
[24] Andrew Lavin and Scott Gray. Fast algorithms for convolutional neural networks. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 4013–4021, 2016.
[25] RonanCollobert,KorayKavukcuoglu,andClémentFarabet.Torch7:Amatlab-likeenvironment for machine learning. In NIPS 2011, 2011.
[26] Richard Gabriel. The rise of worse is better. http://dreamsongs.com/RiseOfWorseIsBetter.html.
[27] Yann LeCun and Corinna Cortes. MNIST handwritten digit database. http://yann.lecun.com/exdb/mnist/.
[28] Oriol Vinyals, Timo Ewalds, Sergey Bartunov, Petko Georgiev, Alexander Sasha Vezhnevets, Michelle Yeo, Alireza Makhzani, Heinrich Küttler, John Agapiou, Julian Schrittwieser, John Quan, Stephen Gaffney, Stig Petersen, Karen Simonyan, Tom Schaul, Hado van Hasselt, David Silver, Timothy P. Lillicrap, Kevin Calderone, Paul Keet, Anthony Brunasso, David Lawrence, Anders Ekermo, Jacob Repp, and Rodney Tsing. Starcraft II: A new challenge for reinforcement learning. CoRR, abs/1708.04782, 2017.
[29] DMLC. Dlpack: Open in memory tensor structure. https://github.com/dmlc/dlpack.
[30] Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer. Automatic differentiation in pytorch. In NIPS Workshop, 2017.
[31] Dan Piponi. Automatic differentiation, C++ templates, and photogrammetry. J. Graphics, GPU, & Game Tools, 9(4):41–55, 2004.
[32] Holger Leuck and Hans-Hellmut Nagel. Automatic differentiation facilitates of-integration into steering-angle-based road vehicle tracking. In 1999 Conference on Computer Vision and Pattern Recognition (CVPR ’99), 23-25 June 1999, Ft. Collins, CO, USA, pages 2360–2365, 1999.
[33] The Python team. The cpython https://wiki.python.org/moin/GlobalInterpreterLock.
[34] Giovanni Petrantoni and Jörg Wollenschläger. Nimtorch. xyz/nimtorch. global interpreter lock. https://github.com/fragcolor-
[35] Austin Huang, Junji Hashimoto, and Sam Stites. Hasktorch. https://github.com/hasktorch/hasktorch.
[36] G. Synnaeve, Z. Lin, J. Gehring, D. Gant, V. Mella, V. Khalidov, N. Carion, and N. Usunier. Forward modeling for partial observation strategy games - a starcraft defogger. In Advances in Neural Information Processing Systems, pages 10761–10771, 2018.
[37] The PyTorch team. Torch Script. https://pytorch.org/docs/stable/jit.html.
[38] Justin Luitjens. Cuda streams. GPU technology conference, 2014.
[39] Emery D. Berger, Kathryn S. McKinley, Robert D. Blumofe, and Paul R. Wilson. Hoard: A scalable memory allocator for multithreaded applications. In Proceedings of the Ninth International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS IX, pages 117–128, New York, NY, USA, 2000. ACM.
[40] J. Evans. A scalable concurrent malloc(3) implementation for freebsd. In In BSDCan — The Technical BSD Conference, May 2006.
[41] S. Ghemawat and P. Menage. Tcmalloc: Thread-caching malloc. 11
[42] Benjamin Recht, Christopher Ré, Stephen J. Wright, and Feng Niu. Hogwild: A lock-free approach to parallelizing stochastic gradient descent. In Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, Granada, Spain., pages 693–701, 2011.
[43] Matthew Hertz and Emery D. Berger. Quantifying the performance of garbage collection vs. explicit memory management. In Proceedings of the 20th Annual ACM SIGPLAN Conference on Object-oriented Programming, Systems, Languages, and Applications, OOPSLA ’05, pages 313–326, New York, NY, USA, 2005. ACM.
[44] ThePyTorchteam.PytorchAutogradProfiler. https://pytorch.org/docs/1.0.1/autograd.html#profiler.
### 4
dockerで機械学習(30) with anaconda(30)「Advanced Deep Learning with Keras」 By Philippe Remy
https://qiita.com/kaizen_nagoya/items/50aadb404ea67abde623

Advanced Deep Learning with TensorFlow 2 and Keras - Second Edition
https://learning.oreilly.com/library/view/advanced-deep-learning/9781838821654/

>Download the example code files
The code bundle for the book is hosted on GitHub at:
https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras
We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!
Download the color images
We also provide color images of figures used in this book. You can download it here: https://static.packt-cdn.com/downloads/9781838821654_ColorImages.pdf.

### 5 
「R Deep Learning Cookbook」 By Philippe Remy dockerで機械学習(79)AI(66)
https://qiita.com/kaizen_nagoya/items/4d6d4b5d9739bcffbf02

R Deep Learning Cookbook
https://learning.oreilly.com/library/view/r-deep-learning/9781787121089/
Resource
https://github.com/packtpublishing/r-deep-learning-cookbook


<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

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

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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。
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小川メソッド　覚え（書きかけ）
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DoCAP（ドゥーキャップ）って何ですか？
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views 20,000越え自己記事一覧
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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
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amazon 殿堂入りNo1レビュアになるまで。仮説(102)
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100以上いいねをいただいた記事16選
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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. 0.01 初稿 　20241124
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