footnote.md

書籍内の脚注

はじめに

• [1] Welcome to Python.org, https://www.python.org/ (Accessed: 30 November 2019).
• [2] Kaggle: Your Home for Data Science, https://www.kaggle.com/ (Accessed: 30 November 2019).
• [3] Titanic: Machine Learning from Disaster, https://www.kaggle.com/c/titanic (Accessed: 30 November 2019).
• [4] Qiita, https://qiita.com/ (Accessed: 30 November 2019).
• [5] Kaggleに登録したら次にやること ～ これだけやれば十分闘える！Titanicの先へ行く入門 10 Kernel ～], https://qiita.com/upura/items/3c10ff6fed4e7c3d70f0 (Accessed: 30 November 2019).
• [6] Kaggle - Qiita, https://qiita.com/tags/kaggle (Accessed: 30 November 2019).
• [7] 村田秀樹, 『Kaggleのチュートリアル』, https://note.mu/currypurin/n/nf390914c721e (Accessed: 30 November 2019).
• [8] GitHub, http://github.com (Accessed: 30 November 2019).
• [9] Docker: Enterprise Container Platform, https://www.docker.com/ (Accessed: 30 November 2019).
• [10] Container Registry - Google Cloud Platform, https://console.cloud.google.com/gcr/images/kaggle-images/GLOBAL/python (Accessed: 30 November 2019).
• [11] PetFinder.my Adoption Prediction, https://www.kaggle.com/c/petfinder-adoption-prediction (Accessed: 30 November 2019).
• [12] Kaggle Days Tokyo, https://kaggledays.com/tokyo/ (Accessed: 30 November 2019).
• [13] 機械学習を用いた日経電子版Proのユーザ分析 データドリブンチームの知られざる取り組み, https://logmi.jp/tech/articles/321077 (Accessed: 30 November 2019).
• [14] Santander Value Prediction Challenge, https://www.kaggle.com/c/santander-value-prediction-challenge (Accessed: 30 November 2019).
• [15] LANL Earthquake Prediction, https://www.kaggle.com/c/LANL-Earthquake-Prediction (Accessed: 30 November 2019).

第1章

• [16] R: The R Project for Statistical Computing, https://www.r-project.org/ (Accessed: 30 November 2019).
• [17] Kaggle Progression System, https://www.kaggle.com/progression (Accessed: 30 November 2019).
• [18] KaggleのGrandmasterやmasterの条件や人数について調べたので、詳細に書きとめます。, http://www.currypurin.com/entry/2018/02/21/011316 (Accessed: 30 November 2019).
• [19] SIGNATE, https://signate.jp/ (Accessed: 30 November 2019).
• [20] 杉山将, 『イラストで学ぶ　機械学習』, 講談社, 2013
• [21] AtCoder：競技プログラミングコンテストを開催する国内最大のサイト, https://atcoder.jp/ (Accessed: 30 November 2019).
• [22] AtCoder に登録したら次にやること ～ これだけ解けば十分闘える！過去問精選 10 問 ～, https://qiita.com/drken/items/fd4e5e3630d0f5859067 (Accessed: 30 November 2019).
• [23] TalkingData AdTracking Fraud Detection Challenge, https://www.kaggle.com/c/talkingdata-adtracking-fraud-detection (Accessed: 30 November 2019).

第2章

• [24] Kaggle API, https://github.com/Kaggle/kaggle-api (Accessed: 30 November 2019).
• [25] kaggle-apiというKaggle公式のapiの使い方をまとめます, http://www.currypurin.com/entry/2018/kaggle-api (Accessed: 30 November 2019).
• [26] NumPy, https://numpy.org/ (Accessed: 30 November 2019).
• [27] Pandas, https://pandas.pydata.org/ (Accessed: 30 November 2019).
• [28] sklearn.preprocessing.StandardScaler, https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html (Accessed: 30 November 2019).
• [29] sklearn.ensemble.RandomForestClassifier, https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html (Accessed: 30 November 2019).
• [30] LightGBM, https://lightgbm.readthedocs.io/en/latest/ (Accessed: 30 November 2019).
• [31] Pandas Profiling, https://github.com/pandas-profiling/pandas-profiling (Accessed: 30 November 2019).
• [32] Santander Customer Transaction Prediction, https://www.kaggle.com/c/santander-customer-transaction-prediction (Accessed: 30 November 2019).
• [33] IEEE-CIS Fraud Detection, https://www.kaggle.com/c/ieee-fraud-detection (Accessed: 30 November 2019).
• [34] Home Credit Default Risk, https://www.kaggle.com/c/home-credit-default-risk (Accessed: 30 November 2019).
• [35] Deterministic neural networks using PyTorch, https://www.kaggle.com/bminixhofer/deterministic-neural-networks-using-pytorch (Accessed: 30 November 2019).
• [36] 門脇大輔・阪田隆司・保坂桂佑・平松雄司,『Kaggleで勝つデータ分析の技術』, 技術評論社, 2019
• [37] 著：Alice Zheng, Amanda Casari, 訳：株式会社ホクソエム, 『機械学習のための特徴量エンジニアリング』, オライリージャパン, 2019
• [38] 最近のKaggleに学ぶテーブルデータの特徴量エンジニアリング, https://www.slideshare.net/mlm_kansai/kaggle-138546659 (Accessed: 30 November 2019).
• [39] 【随時更新】Kaggleテーブルデータコンペできっと役立つTipsまとめ, https://naotaka1128.hatenadiary.jp/entry/kaggle-compe-tips (Accessed: 30 November 2019).
• [40] nejumi/kaggle_memo, https://github.com/nejumi/kaggle_memo (Accessed: 30 November 2019).
• [41] 本橋智光,『前処理大全』, 技術評論社, 2018
• [42] Instacart Market Basket Analysis, https://www.kaggle.com/c/instacart-market-basket-analysis (Accessed: 30 November 2019).
• [43] 第2回：「Kaggle」の面白さとは--食品宅配サービスの購買予測コンペで考える -, https://japan.zdnet.com/article/35124706/ (Accessed: 30 November 2019).
• [44] PLAsTiCC Astronomical Classification, https://www.kaggle.com/c/PLAsTiCC-2018 (Accessed: 30 November 2019).
• [45] 半田利弘, 『基礎からわかる天文学』, 誠文堂新光社, 2011
• [46] Python-package Introduction, https://lightgbm.readthedocs.io/en/latest/Python-Intro.html (Accessed: 30 November 2019).
• [47] Supervised learning, https://scikit-learn.org/stable/supervised_learning.html (Accessed: 30 November 2019).
• [48] lightgbm カテゴリカル変数と欠損値の扱いについて+α, https://tebasakisan.hatenadiary.com/entry/2019/01/27/222102 (Accessed: 30 November 2019).
• [49] XGBoost, https://xgboost.readthedocs.io/en/latest/ (Accessed: 30 November 2019).
• [50] CatBoost, https://catboost.ai/ (Accessed: 30 November 2019).
• [51] PyTorch, https://pytorch.org/ (Accessed: 30 November 2019).
• [52] TensorFlow, https://www.tensorflow.org/ (Accessed: 30 November 2019).
• [53] LightGBM Parameters, https://lightgbm.readthedocs.io/en/latest/Parameters.html (Accessed: 30 November 2019).
• [54] LightGBM Parameters-Tuning, https://lightgbm.readthedocs.io/en/latest/Parameters-Tuning.html (Accessed: 30 November 2019).
• [55] sklearn.model_selection.GridSearchCV, https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html (Accessed: 30 November 2019).
• [56] Bayesian Optimization, https://github.com/fmfn/BayesianOptimization (Accessed: 30 November 2019).
• [57] Hyperopt, https://github.com/hyperopt/hyperopt (Accessed: 30 November 2019).
• [58] Optuna, https://optuna.org/ (Accessed: 30 November 2019).
• [59] Optuna Trial, https://optuna.readthedocs.io/en/latest/reference/trial.html (Accessed: 30 November 2019).
• [60] Optunaでrandomのseedを固定する方法, https://qiita.com/phorizon20/items/1b795beb202c2dc378ed (Accessed: 30 November 2019).
• [61] 勾配ブースティングで大事なパラメータの気持ち, https://nykergoto.hatenablog.jp/entry/2019/03/29/勾配ブースティングで大事なパラメータの気持ち (Accessed: 30 November 2019).
• [62] 有名ライブラリと比較したLightGBMの現在, https://alphaimpact.jp/downloads/pydata20190927.pdf (Accessed: 30 November 2019).
• [63] Recruit Restaurant Visitor Forecasting, https://www.kaggle.com/c/recruit-restaurant-visitor-forecasting (Accessed: 30 November 2019).
• [64] Neko kin, https://www.slideshare.net/ShotaOkubo/neko-kin-96769953 (Accessed: 30 November 2019).
• [65] sklearn.model_selection.TimeSeriesSplit, https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.TimeSeriesSplit.html (Accessed: 30 November 2019).
• [66] State Farm Distracted Driver Detection, https://www.kaggle.com/c/state-farm-distracted-driver-detection (Accessed: 30 November 2019).
• [67] Kaggle State Farm Distracted Driver Detection, https://speakerdeck.com/iwiwi/kaggle-state-farm-distracted-driver-detection (Accessed: 30 November 2019).
• [68] sklearn.model_selection.GroupKFold, https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GroupKFold.html (Accessed: 30 November 2019).
• [69] Profiling Top Kagglers: Bestfitting, Currently #1 in the World, https://medium.com/kaggle-blog/profiling-top-kagglers-bestfitting-currently-1-in-the-world-58cc0e187b (Accessed: 30 November 2019).
• [70] Kaggle Ensembling Guide, https://mlwave.com/kaggle-ensembling-guide/ (Accessed: 30 November 2019).
• [71] Avito Demand Prediction Challenge, https://www.kaggle.com/c/avito-demand-prediction (Accessed: 30 November 2019).
• [72] Kaggle Avito Demand Prediction Challenge 9th Place Solution, https://www.slideshare.net/JinZhan/kaggle-avito-demand-prediction-challenge-9th-place-solution-124500050 (Accessed: 30 November 2019).
• [73] The BigChaos Solution to the Netflix Grand Prize, https://www.netflixprize.com/assets/GrandPrize2009_BPC_BigChaos.pdf (Accessed: 30 November 2019).

第3章

• [74] Introduction to Manual Feature Engineering, https://www.kaggle.com/willkoehrsen/introduction-to-manual-feature-engineering (Accessed: 30 November 2019).
• [75] 第9回：Kaggleの「画像コンペ」とは--取り組み方と面白さを読み解く, https://japan.zdnet.com/article/35140207/ (Accessed: 30 November 2019).
• [76] Adversarial Example, https://arxiv.org/abs/1312.6199 (Accessed: 30 November 2019).
• [77] Generative Adversarial Network(GAN), https://arxiv.org/abs/1406.2661 (Accessed: 30 November 2019).
• [78] CS231n: Convolutional Neural Networks for Visual Recognition, http://cs231n.stanford.edu/ (Accessed: 30 November 2019).
• [79] Lecture 11: Detection and Segmentation, http://cs231n.stanford.edu/slides/2018/cs231n_2018_lecture11.pdf (Accessed: 30 November 2019).
• [80] Neural Information Processing Systems (NeurIPS), https://nips.cc/ (Accessed: 30 November 2019).
• [81] NIPS 2017: Non-targeted Adversarial Attack, https://www.kaggle.com/c/nips-2017-non-targeted-adversarial-attack/ (Accessed: 30 November 2019).
• [82] NIPS’17 Adversarial Learning Competition に参戦しました, https://research.preferred.jp/2018/04/nips17-adversarial-learning-competition/ (Accessed: 30 November 2019).
• [83] Explaining and Harnessing Adversarial Examples, https://arxiv.org/abs/1412.6572 (Accessed: 30 November 2019).
• [84] Generative Dog Images, https://www.kaggle.com/c/generative-dog-images (Accessed: 30 November 2019).
• [85] An intuitive introduction to Generative Adversarial Networks (GANs), https://www.freecodecamp.org/news/an-intuitive-introduction-to-generative-adversarial-networks-gans-7a2264a81394/ (Accessed: 30 November 2019).
• [86] Generative Dog Images, https://speakerdeck.com/hirune924/generative-dog-images (Accessed: 30 November 2019).
• [87] TRAINING A CLASSIFIER, https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html (Accessed: 30 November 2019).
• [88] CIFAR10, https://www.cs.toronto.edu/~kriz/cifar.html (Accessed: 30 November 2019).
• [89] 原田達也, 『画像認識』, 講談社, 2017
• [90] Distinctive Image Features from Scale-Invariant Keypoints, https://www.robots.ox.ac.uk/~vgg/research/affine/det_eval_files/lowe_ijcv2004.pdf (Accessed: 30 November 2019).
• [91] iMet 7th place solution & my approach to image data competition, https://speakerdeck.com/phalanx/imet-7th-place-solution-and-my-approach-to-image-data-competition?slide=30 (Accessed: 30 November 2019).
• [92] Convolutional Neural Network (CNN), https://www.deeplearningbook.org/front_matter.pdf (Accessed: 30 November 2019).
• [93] APTOS 2019 Blindness Detection, https://www.kaggle.com/c/aptos2019-blindness-detection (Accessed: 30 November 2019).
• [94] TensorFlow 2.0 Question Answering, https://www.kaggle.com/c/tensorflow2-question-answering (Accessed: 30 November 2019).
• [95] Quora Insincere Questions Classification, https://www.kaggle.com/c/quora-insincere-questions-classification/ (Accessed: 30 November 2019).
• [96] Jigsaw Unintended Bias in Toxicity Classification, https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification (Accessed: 30 November 2019).
• [97] 絵で理解するWord2vecの仕組み, https://qiita.com/Hironsan/items/11b388575a058dc8a46a (Accessed: 30 November 2019).
• [98] word2vec（Skip-Gram Model）の仕組みを恐らく日本一簡潔にまとめてみたつもり, https://www.randpy.tokyo/entry/word2vec_skip_gram_model (Accessed: 30 November 2019).
• [99] Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms, https://arxiv.org/abs/1805.09843 (Accessed: 30 November 2019).
• [100] Approaching (Almost) Any NLP Problem on Kaggle, https://www.kaggle.com/abhishek/approaching-almost-any-nlp-problem-on-kaggle (Accessed: 30 November 2019).
• [101] BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, https://arxiv.org/abs/1810.04805 (Accessed: 30 November 2019).
• [102] XLNet: Generalized Autoregressive Pretraining for Language Understanding, https://arxiv.org/abs/1906.08237 (Accessed: 30 November 2019).
• [103] ja.text8, https://github.com/Hironsan/ja.text8 (Accessed: 30 November 2019).
• [104] 日本語版text8コーパスを作って分散表現を学習する, https://hironsan.hatenablog.com/entry/japanese-text8-corpus (Accessed: 30 November 2019).

第4章

• [105] GCPとDockerでKaggle用計算環境構築, https://qiita.com/lain21/items/a33a39d465cd08b662f1 (Accessed: 30 November 2019).
• [106] Kaggle用のGCP環境を手軽に構築, https://qiita.com/hiromu166/items/2a738f7be49d88d8b599 (Accessed: 30 November 2019).
• [107] Kaggleの画像コンペのためのGCPインスタンス作成手順（2019年10月版）, https://currypurin.qrunch.io/entries/T9iGWHdsiI6o2wke (Accessed: 30 November 2019).

4.4 お勧めの資料・文献・リンク

• 4.4.1 kaggler-ja slack, https://kaggler-ja.herokuapp.com/
• 4.4.2 kaggler-ja wiki, http://kaggler-ja-wiki.herokuapp.com/
• 4.4.3 門脇大輔ら,『Kaggleで勝つデータ分析の技術』, 技術評論社, 2019, https://gihyo.jp/book/2019/978-4-297-10843-4
• 4.4.4 Kaggle Tokyo Meetupの資料・動画

回	URL
第1回	https://atnd.org/events/74953
第2回	https://techplay.jp/event/613561
第3回	http://yutori-datascience.hatenablog.com/entry/2017/10/29/205433
第4回	https://connpass.com/event/82458/presentation/
第4回（動画）	https://www.youtube.com/watch?v=VMjnhGW2MgU&list=PLkBjLQIG@{}EjJlciM9lEz1AsuZZ8lDgyxDu
第5回	https://connpass.com/event/105298/presentation/
第6回	https://connpass.com/event/132935/