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スパースモデリングを特集した岩波データサイエンスVol.5の勉強会用の資料です。
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スパースモデリングを特集した岩波データサイエンスVol.5の勉強会用の資料で、題材は、伊庭氏の「モデル選択超速習」です。間違いの訂正、補足、コメント等ありましたら、よろしくお願いします。
岩波データサイエンス_Vol.5_勉強会資料01
岩波データサイエンス_Vol.5_勉強会資料01
goony0101
スパースモデリングを特集した岩波データサイエンスVol.5の勉強会用の資料です。今回は、池田思朗氏の「スパース性を用いた推定」を題材としています。
岩波データサイエンス_Vol.5_勉強会資料02
岩波データサイエンス_Vol.5_勉強会資料02
goony0101
第46回東京R勉強会 #TokyoRの資料です。
順序データでもベイズモデリング
順序データでもベイズモデリング
. .
JaLTER 公開シンポジウム2015「生物・生態系情報の統合と時系列データの解析~生物や生態系の変化を読み解く~」における発表ファイル
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Rパッケージ“KFAS”を使った時系列データの解析方法
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機械学習のためのベイズ最適化入門
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https://www.hubspot.com/state-of-marketing · Scaling relationships and proving ROI · Social media is the place for search, sales, and service · Authentic influencer partnerships fuel brand growth · The strongest connections happen via call, click, chat, and camera. · Time saved with AI leads to more creative work · Seeking: A single source of truth · TLDR; Get on social, try AI, and align your systems. · More human marketing, powered by robots
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スパースモデリングを特集した岩波データサイエンスVol.5の勉強会用の資料で、題材は、伊庭氏の「モデル選択超速習」です。間違いの訂正、補足、コメント等ありましたら、よろしくお願いします。
岩波データサイエンス_Vol.5_勉強会資料01
岩波データサイエンス_Vol.5_勉強会資料01
goony0101
スパースモデリングを特集した岩波データサイエンスVol.5の勉強会用の資料です。今回は、池田思朗氏の「スパース性を用いた推定」を題材としています。
岩波データサイエンス_Vol.5_勉強会資料02
岩波データサイエンス_Vol.5_勉強会資料02
goony0101
第46回東京R勉強会 #TokyoRの資料です。
順序データでもベイズモデリング
順序データでもベイズモデリング
. .
JaLTER 公開シンポジウム2015「生物・生態系情報の統合と時系列データの解析~生物や生態系の変化を読み解く~」における発表ファイル
Rパッケージ“KFAS”を使った時系列データの解析方法
Rパッケージ“KFAS”を使った時系列データの解析方法
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ChatGPT is a revolutionary addition to the world since its introduction in 2022. A big shift in the sector of information gathering and processing happened because of this chatbot. What is the story of ChatGPT? How is the bot responding to prompts and generating contents? Swipe through these slides prepared by Expeed Software, a web development company regarding the development and technical intricacies of ChatGPT!
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Mental health has been in the news quite a bit lately. Dozens of U.S. states are currently suing Meta for contributing to the youth mental health crisis by inserting addictive features into their products, while the U.S. Surgeon General is touring the nation to bring awareness to the growing epidemic of loneliness and isolation. The country has endured periods of low national morale, such as in the 1970s when high inflation and the energy crisis worsened public sentiment following the Vietnam War. The current mood, however, feels different. Gallup recently reported that national mental health is at an all-time low, with few bright spots to lift spirits. To better understand how Americans are feeling and their attitudes towards mental health in general, ThinkNow conducted a nationally representative quantitative survey of 1,500 respondents and found some interesting differences among ethnic, age and gender groups. Technology For example, 52% agree that technology and social media have a negative impact on mental health, but when broken out by race, 61% of Whites felt technology had a negative effect, and only 48% of Hispanics thought it did. While technology has helped us keep in touch with friends and family in faraway places, it appears to have degraded our ability to connect in person. Staying connected online is a double-edged sword since the same news feed that brings us pictures of the grandkids and fluffy kittens also feeds us news about the wars in Israel and Ukraine, the dysfunction in Washington, the latest mass shooting and the climate crisis. Hispanics may have a built-in defense against the isolation technology breeds, owing to their large, multigenerational households, strong social support systems, and tendency to use social media to stay connected with relatives abroad. Age and Gender When asked how individuals rate their mental health, men rate it higher than women by 11 percentage points, and Baby Boomers rank it highest at 83%, saying it’s good or excellent vs. 57% of Gen Z saying the same. Gen Z spends the most amount of time on social media, so the notion that social media negatively affects mental health appears to be correlated. Unfortunately, Gen Z is also the generation that’s least comfortable discussing mental health concerns with healthcare professionals. Only 40% of them state they’re comfortable discussing their issues with a professional compared to 60% of Millennials and 65% of Boomers. Race Affects Attitudes As seen in previous research conducted by ThinkNow, Asian Americans lag other groups when it comes to awareness of mental health issues. Twenty-four percent of Asian Americans believe that having a mental health issue is a sign of weakness compared to the 16% average for all groups. Asians are also considerably less likely to be aware of mental health services in their communities (42% vs. 55%) and most likely to seek out information on social media (51% vs. 35%).
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Everyone is in agreement that ChatGPT (and other generative AI tools) will shape the future of work. Yet there is little consensus on exactly how, when, and to what extent this technology will change our world. Businesses that extract maximum value from ChatGPT will use it as a collaborative tool for everything from brainstorming to technical maintenance. For individuals, now is the time to pinpoint the skills the future professional will need to thrive in the AI age. Check out this presentation to understand what ChatGPT is, how it will shape the future of work, and how you can prepare to take advantage.
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Mental health has been in the news quite a bit lately. Dozens of U.S. states are currently suing Meta for contributing to the youth mental health crisis by inserting addictive features into their products, while the U.S. Surgeon General is touring the nation to bring awareness to the growing epidemic of loneliness and isolation. The country has endured periods of low national morale, such as in the 1970s when high inflation and the energy crisis worsened public sentiment following the Vietnam War. The current mood, however, feels different. Gallup recently reported that national mental health is at an all-time low, with few bright spots to lift spirits. To better understand how Americans are feeling and their attitudes towards mental health in general, ThinkNow conducted a nationally representative quantitative survey of 1,500 respondents and found some interesting differences among ethnic, age and gender groups. Technology For example, 52% agree that technology and social media have a negative impact on mental health, but when broken out by race, 61% of Whites felt technology had a negative effect, and only 48% of Hispanics thought it did. While technology has helped us keep in touch with friends and family in faraway places, it appears to have degraded our ability to connect in person. Staying connected online is a double-edged sword since the same news feed that brings us pictures of the grandkids and fluffy kittens also feeds us news about the wars in Israel and Ukraine, the dysfunction in Washington, the latest mass shooting and the climate crisis. Hispanics may have a built-in defense against the isolation technology breeds, owing to their large, multigenerational households, strong social support systems, and tendency to use social media to stay connected with relatives abroad. Age and Gender When asked how individuals rate their mental health, men rate it higher than women by 11 percentage points, and Baby Boomers rank it highest at 83%, saying it’s good or excellent vs. 57% of Gen Z saying the same. Gen Z spends the most amount of time on social media, so the notion that social media negatively affects mental health appears to be correlated. Unfortunately, Gen Z is also the generation that’s least comfortable discussing mental health concerns with healthcare professionals. Only 40% of them state they’re comfortable discussing their issues with a professional compared to 60% of Millennials and 65% of Boomers. Race Affects Attitudes As seen in previous research conducted by ThinkNow, Asian Americans lag other groups when it comes to awareness of mental health issues. Twenty-four percent of Asian Americans believe that having a mental health issue is a sign of weakness compared to the 16% average for all groups. Asians are also considerably less likely to be aware of mental health services in their communities (42% vs. 55%) and most likely to seek out information on social media (51% vs. 35%).
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From their humble beginnings in 1984, TED has grown into the world’s most powerful amplifier for speakers and thought-leaders to share their ideas. They have over 2,400 filmed talks (not including the 30,000+ TEDx videos) freely available online, and have hosted over 17,500 events around the world. With over one billion views in a year, it’s no wonder that so many speakers are looking to TED for ideas on how to share their message more effectively. The article “5 Public-Speaking Tips TED Gives Its Speakers”, by Carmine Gallo for Forbes, gives speakers five practical ways to connect with their audience, and effectively share their ideas on stage. Whether you are gearing up to get on a TED stage yourself, or just want to master the skills that so many of their speakers possess, these tips and quotes from Chris Anderson, the TED Talks Curator, will encourage you to make the most impactful impression on your audience. See the full article and more summaries like this on SpeakerHub here: https://speakerhub.com/blog/5-presentation-tips-ted-gives-its-speakers See the original article on Forbes here: http://www.forbes.com/forbes/welcome/?toURL=http://www.forbes.com/sites/carminegallo/2016/05/06/5-public-speaking-tips-ted-gives-its-speakers/&refURL=&referrer=#5c07a8221d9b
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Everyone is in agreement that ChatGPT (and other generative AI tools) will shape the future of work. Yet there is little consensus on exactly how, when, and to what extent this technology will change our world. Businesses that extract maximum value from ChatGPT will use it as a collaborative tool for everything from brainstorming to technical maintenance. For individuals, now is the time to pinpoint the skills the future professional will need to thrive in the AI age. Check out this presentation to understand what ChatGPT is, how it will shape the future of work, and how you can prepare to take advantage.
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岩波データサイエンス_Vol.5_勉強会資料00
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岩波データサイエンス Vol.5 [特集] スパースモデリングと 多変量データ解析 2017/3/20 岩波データサイエンス
Vol.5 1
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この本 • 岩波データサイエンス Vol.5 •
[特集] スパースモデリングと多変量 データ解析 – 岩波データサイエンス刊⾏委 員会 編 – 2017年2⽉刊⾏ – 岩波書店 2017/3/20 岩波データサイエンス Vol.5 2
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目次 • 特集「スパースモデリングと多変量 データ解析」 • 池田・伊庭・麻生 <基礎編> •
モデル選択超速習 – AICからスパースまで • 伊庭幸人 • スパース性を用いた推定 • 池田思朗 • スパースモデリングを体験してみる • 岩波データサイエンス刊⾏委員会 <展開編> • 依存関係にスパース性を入れる – グラフィカルlassoの話 • 井手剛 • 画像処理とスパース • 本谷秀堅 • 時間遷移のスパース性 – マーケットシェアの遷移を捉える スパースグラフモデリング • 日野英逸 <応用編> • ⾏列データの分解 • 麻生英樹 – <コラム>⾏列のトレースノルム – 麻生英樹 • ⾏列分解をリコメンデーションに活かす – Rによる実践例 • 尾崎隆 2017/3/20 岩波データサイエンス Vol.5 3
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「スパースモデリングと多変量データ解析」 • 10年前の海外の理論分野の学会 » (統計学、情報理論、信号処理等) –
“スパースモデリング”で埋め尽くされていた – その中心 ・・・ “L1ノルムによる正則化” – 「L2ノルムが20世紀のノルムだとすればL1ノルムは21世紀のノルムだ」 (Candes) (…いささか大げさ?) • L1ノルム正則化を用いることで それまで諦めていた情報処理が可能になったことは事実 2017/3/20 岩波データサイエンス Vol.5 4
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「スパースモデリングと多変量データ解析」 • 【海外】 – 理論的枠組みが確⽴すると、、、 –
⇒ 他の分野での問題に応用 – ⇒ あらゆるデータ解析において取り込まれる – ⇒“スパースモデリング”は基礎的な方法としてすっかり定着 • 【国内】 〜 状況は少し異なる – 1980年代後半に石川眞澄の先駆的な研究はあったが、、、 ⇒ 流⾏を作るまでには⾄らず・・・ – 2010年代から“スパースモデリング”注目 ⇒今、まさに普及している時期 2017/3/20 岩波データサイエンス Vol.5 5
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「スパースモデリングと多変量データ解析」 • 本特集では スパースモデリングを概観 –
基礎的な枠組みを示し、 何ができて、どうやればいいのか を解説 – 応用、実践もあり • ビジネスへの応用もすでに始まっているが、 • 実際の普及はまだまだこれからだろう • ⇒本書が、応用を目指すDSの役に⽴てれば・・・ • できるだけ実践的な面を強調(≠数理⾯強調) • ビジネスへの応用を意識した⾏列分解⼿法の解説も加えた 2017/3/20 岩波データサイエンス Vol.5 6
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「スパースモデリングと多変量データ解析」 <基礎編> • 「モデル選択超速習」(伊庭) • “スパース以前”のモデル選択をめぐる諸問題 •
⇒スパースモデリングの位置付け • 「スパース性を用いた推定」(池田) • “本特集の要” • 「lasso」(・・・スパースモデリングの代表格)の 入門、実用上の注意、バリエーション、数値計算手法 • 「スパースモデリングを体験してみる」(岩波DS刊⾏委員会) • R での実⾏例 2017/3/20 岩波データサイエンス Vol.5 7
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「スパースモデリングと多変量データ解析」 <展開編> • 「依存関係にスパース性を入れる」(井手) • ガウス型のグラフィカルモデル
(→ Vol.3参照) • 考えるのは、“直接的な影響”と“間接的なものを含んだ影響”の違い • 「画像処理とスパース」(本谷) • “辞書”の概念を用いた画像処理において スパースの考え方がどのように使われるか、が示される • 「時間遷移のスパース性」(日野) • マルコフ連鎖モデルの遷移⾏列の推定にスパースモデルを応用 • 最近の研究;ビジネスデータへの応用例としても興味深い 2017/3/20 岩波データサイエンス Vol.5 8
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「スパースモデリングと多変量データ解析」 <応用編> • 「⾏列データの分解」(麻生) • 特異値分解や主成分分析などの古典的話題からはじめて、 •
スパースモデリングや非負値の制約付きの分解までをコンパクトに • “知識の整理”にもお勧め • 「⾏列分解をリコメンデーションに活かす」(尾崎) • ⾏列分解の“実践編” • R のパッケージを利用;リコメンデーションへの応用例 2017/3/20 岩波データサイエンス Vol.5 9
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その他の参考文献 • [1]荒木孝治(2013), 罰則付き回帰とデータ解析環境R,
オペレーションズリサーチ, 2013年5⽉号 • [2]Hastie, T., and Tibshirani, R. (2014), 統計的学習の基礎: データマイニング・推論・予測, 共⽴出版 • [3]廣瀬慧(2014), Lasso タイプの正則化法に基づくスパース推定法を用いた超高次元データ解析, 数理解析研究所講究録 1908 : 57-77 (http://www.kurims.kyoto-u.ac.jp/~kyodo/kokyuroku/contents/pdf/1908-05.pdf) • [4]⿅島久嗣, 「数理情報⼯学特論第⼀【機械学習とデータマイニング】1章:概論(3)」講義資 料(www.geocities.co.jp/kashi_pong/1-3.pdf) • [5]冨岡亮太(2015), スパース性に基づく機械学習, 講談社 • [6]山下信雄(2013), Coordinate Descent 法について, 「最適化の基盤とフロンティア」研究部会 講演資料 (https://www.slideshare.net/amp-kyoto/coordinate-descent) • [7] Das, K. and Sobel, M.(2015), Dirichlet Lasso: A Bayesian approach to variable selection, Statistical Modelling, vol.15 (3) (2015), 215–232 • [8] Efron, B., Hastie, T., Johnstone, I., and Tibshirani, R. (2004), Least Angle Regression(with discussion), The Annals of Statistics, 32, 407–499. 2017/3/20 岩波データサイエンス Vol.5 10
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