投稿時間:2021-11-03 08:37:28 RSSフィード2021-11-03 08:00 分まとめ(50件)

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TECH	Engadget Japanese	ここでしか出逢えない地酒セットで蔵元へエールを。長野県酒蔵応援プロジェクト第一弾「ふる里」	https://japanese.engadget.com/nagano-sake-brewery-furusato-224029174.html	長野県酒蔵応援プロジェクト第一弾「ふる里」今回の長野県酒蔵応援プロジェクト第一弾「ふる里」でお届けするラインナップこのプロジェクトでしか味わえない、お米作りからこだわった“超貴重な本セット今回は販売されていないお酒と、特別に誂えてもらったお酒だけで本セットを作ることができました。	2021-11-02 22:40:29
IT	ITmedia 総合記事一覧	[ITmedia News] Meta（旧Facebook）、Facebookでの顔認識機能停止へ　10億人以上のテンプレートは削除	https://www.itmedia.co.jp/news/articles/2111/03/news030.html	facebook	2021-11-03 07:19:00
TECH	Techable（テッカブル）	16日間で平均40%点数アップ！ 使うほどに最適化するAI型教材「Qubena」が追加＆アップデート	https://techable.jp/archives/165830	compass	2021-11-02 22:00:43
AWS	AWS Big Data Blog	Query hierarchical data models within Amazon Redshift	https://aws.amazon.com/blogs/big-data/query-hierarchical-data-models-within-amazon-redshift/	Query hierarchical data models within Amazon RedshiftIn a hierarchical database model information is stored in a tree like structure or parent child structure where each record can have a single parent but many children Hierarchical databases are useful when you need to represent data in a tree like hierarchy The perfect example of a hierarchical data model is the navigation file and folders or …	2021-11-02 22:30:16
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	Proxmoxの管理コンソールに入れない	https://teratail.com/questions/367484?rss=all	Proxmoxの管理コンソールに入れない前提・実現したいこと実機にproxmoxを入れる前に試しにVirtualBoxに入れてみましたがWEBの管理コンソールにアクセスできませんでした。	2021-11-03 07:58:20
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	Gitでの「＄ git branch -M main」を取り消す方法について	https://teratail.com/questions/367483?rss=all	Gitでの「gitbranchMmain」を取り消す方法について質問内容Macのターミナルを用いてリモートリポジトリとローカルリポジトリを紐づけてローカルリポジトリをpushしてcloneしようとしていました。	2021-11-03 07:57:35
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	vbaでメール（Outlook.Application）を作成する際にファイル名を変更して添付したい	https://teratail.com/questions/367482?rss=all	vbaでメールOutlookApplicationを作成する際にファイル名を変更して添付したい下記のようなコードを作り、csvに記載のある情報をもとに自動的にメールを作成することができました。	2021-11-03 07:56:23
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	MiddlemanでYAMLをレンダリングする方法	https://teratail.com/questions/367481?rss=all	MiddlemanでYAMLをレンダリングする方法yamlで書かれたファイルがあり、hamlを使ってmiddlemanでレンダリングしたいと思っています。	2021-11-03 07:47:06
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	build.gradleでエラーが出てしまいます	https://teratail.com/questions/367480?rss=all	buildgradleでエラーが出てしまいますお世話になります。	2021-11-03 07:10:04
技術ブログ	Developers.IO	GitHub Actionsで発行プロファイルを使わずにAzure App Serviceへデプロイする	https://dev.classmethod.jp/articles/github-actions-oidc-appservice-deploy/	azure	2021-11-02 22:30:09
海外TECH	Ars Technica	Hate broccoli and cauliflower? Your microbiome might be partially to blame	https://arstechnica.com/?p=1809324	dislike	2021-11-02 22:30:42
海外TECH	DEV Community	End-to-end machine learning lifecycle	https://dev.to/mage_ai/end-to-end-machine-learning-lifecycle-1p0i	End to end machine learning lifecycle TLDRA machine learning ML project requires collaboration across multiple roles in a business We ll introduce the high level steps of what the end to end ML lifecycle looks like and how different roles can collaborate to complete the ML project OutlineIntroductionDefine problemCollect dataPrepare dataTrain evaluate and improve modelDeploy and integrate modelMonitor modelConclusion IntroductionMachine learning is a powerful tool to help solve different problems in your business The article “Building your first machine learning model gives you basic ideas of what it takes to build a machine learning model In this article we ll talk about what the end to end machine learning project lifecycle looks like in a real business The chart below shows the high level steps from project initiation to completion Completing a ML project requires collaboration across multiple roles including product manager product developer data scientist and MLOps engineer Failing to accurately execute on any one of these steps will result in misleading insights or models with no practical value Define problemWhen talking about machine learning people usually have high expectations on what it can achieve Before starting a machine learning project the product team should collaborate to come up with the problem definition Here are some questions that should be clarified at this step What s the problem Machine learning can be used to solve various problems e g reduce manual work rank products etc Before starting the project we need to clearly define the problem and expected outcome We should think about whether this is a valuable problem to solve and estimate how much value machine learning can bring How should we measure the success of the model There are different objectives when using machine learning We should be clear how to measure the success of the model based on different objectives If we want to use a machine learning model to reduce manual work we should measure whether the model can give results as well as a human does If we want to use machine learning to rank products on a website we can measure whether we get a higher click through rate after using the model to rank the products Do we have enough data to build the model Now that we have the idea we need to think about one practical thing do we have the data The machine learning model learns from the past data and predicts for the new data If you don t have enough data machine learning won t be a good choice for you Collect dataNo matter what model we want to build we first need to collect two types of data The first type of data contains the labels the target variable we want to predict or can be used to create the labels The second type of data can be used to generate features that ll affect the model predictions For example if we want to build a model to predict whether a user will churn We at least need to get a table which contains data indicating whether the user has churned In addition we also want to collect user events to generate more features which can contribute to the model predictions Product developers are usually responsible for collecting the data after getting data requirements from data scientists If you have a good habit of logging the events then you ll be relieved when building machine learning models If you don t have good logging in your product start doing it This data will help you understand your product better even if you don t have immediate needs for machine learning models Next the work can be handed over to data scientists to prepare the data and train the model Prepare dataData preparation is one of the most complex steps in the machine learning lifecycle which is also called “Feature Engineering If you don t have data processing experience and want to learn it this Developer Education series will be a good resource for you Here are the basic steps of feature engineering Create labelsIn machine learning “label is the target variable you want to predict with the model To prepare the data for model training we need to identify whether we have a label column in our dataset If there s no explicit label column in our datasets we need to create labels first Create featuresMachine learning algorithms learn from the features Here are some ways to create features Expand the existing features For example you can expand your date feature to “year “month “day and “days since holiday features Aggregate the events feature One example is to count the number of user events over the past days days or days Another example is to count the number of page view events from Google and Facebook respectively Impute encode and scaleAfter creating labels and features we need to get our data ready for machine learning algorithms Impute The real world datasets usually have missing values Machine learning algorithms don t handle missing values well Thus we need to fill in the missing value with data inferred from existing data Encode Machine learning algorithms require data to be numbers Thus we need to convert the text features to numbers Scale Numbers with larger ranges will have a higher impact on the model output We need to adjust the values of number columns to fall within similar ranges so that large numbers such as seconds since epoch don t affect the prediction disproportionately as much as smaller values such as age Train evaluate and improve modelAfter data is prepared we split the dataset into a training set and a test set select an algorithm and then start training the model with the training set We briefly introduced some machine learning algorithms in the Fundamentals of being an AI ML sorcerer supreme article We ll discuss different algorithms in detail in a future blog article After model training completes we need to evaluate the model s performance with the test set We use Precision and Recall to evaluate a classification model s performance and use Mean Absolute Error and Root Mean Squared Error to evaluate a regression model s performance The article “How to improve the performance of a machine learning ML model introduced the strategies for improving models including comparing multiple algorithms hyperparameter tuning and more feature engineering Deploy and integrate modelOnce you re done with the model training and are satisfied with the model performance the data scientist can now hand over the model to the MLOps engineer to deploy the model to production Then the product developer will integrate the model into the product There are generally two ways to integrate models and make predictions online predictions and offline batch predictions Online predictionFor online prediction we can deploy the model to an online web service and make API calls to the online service to get predictions This is useful when we need to get real time predictions e g realtime product ranking Offline batch predictionFor other models we don t necessarily need to get real time predictions We can use an offline batch prediction job to get predictions for a large amount of data points on a regular basis These predictions are then stored in a database and can be made available to developers or end users For example for the demand forecast model we can estimate the demand for products on a daily basis for upcoming one year with an offline batch prediction job ExperimentationAfter integrating the model into production you can run an experiment to evaluate the model performance with real production traffic For example if you build a ranking model for your e commerce website You can split the website traffic into Half of the users will see the products in the original order control group Another half of the users will see products in the ranked order determined by the ranking model treatment group We can compare the target metrics e g click through rate between the users in the control and the treatment groups Monitor modelCongratulations With the team s hard work your model is finally live You evaluated the model via experimentation and got the expected outcome Is this everything you need to do for the model The answer is no Model performance can degrade over time It s important to set up a good monitoring system to make sure the model works correctly in production over time Multiple things could go wrong in production One of the most common issues is data drift which means the distribution of the target variable or the input data changes over time The model monitoring system should monitor the model performance with production data detect the data drift issue and provide feedback for further model improvement e g model retrain Stay tuned for a future article about model monitoring ConclusionThe whole machine learning lifecycle is a lengthy process which requires expertise across multiple roles The product team defines the problem The product developer collects the data The data scientist prepares the data and trains the model The MLOps engineer deploys the model into production The product developer integrates the model into the product The MLOps engineer sets up the model monitoring system If you wonder whether there s a way to simplify the process Mage helps handle all the work from “prepare data to “monitor model Mage also provides suggestions on what type of problems you can solve with ML and what data is needed	2021-11-02 22:46:28
海外TECH	DEV Community	Top 7 Featured DEV Posts from the Past Week	https://dev.to/devteam/top-7-featured-dev-posts-from-the-past-week-35fb	Top Featured DEV Posts from the Past WeekEvery Tuesday we round up the previous week s top posts based on traffic engagement and a hint of editorial curation The typical week starts on Monday and ends on Sunday but don t worry we take into account posts that are published later in the week It s all in the documentation sgoulas built an e commerce site and kept a detailed development diary over the span of months Super fascinating and helpful I created an e commerce site from scratch and kept a development diary over the cource of months sgoulas・Oct ・ min read react javascript webdev nextjs Class is in session mustapha covers the tools we can use to create classes how to create objects using classes and lots more A solid guide to ES classes ️ A deep dive into ES Classes Mustapha Aouas・Oct ・ min read javascript webdev programming Take care of your own system abdullah alhariri has some sound advice if you re working on leading a more balanced and impactful developer The Secrets behind highly effective programmers Abdullah Alhariri・Oct ・ min read programming beginners productivity Optimizing Your Linkedin presenceLike it or not Linkedin remains an incredibly helpful tool for landing jobs in nearly every field ーincluding software development If you need to improve the quality of your profile techieeliot has some pointers Want to have tech recruiters find you Sixteen important things to remember Eliot Sanford・Oct ・ min read webdev beginners career codenewbie It s not just about writing code and other enlightening lessons about your first software development gig via sanspanic Things That Might Surprise You About Your First Software Engineering Job Sandra Spanik・Oct ・ min read career webdev programming productivity Blockchain Learning by doingThe community loved this article about creating blockchain in lines of code by freakcdev Great job Creating a blockchain in lines of Javascript Phu Minh・Oct ・ min read blockchain javascript tutorial watercooler Hacktoberfest lives onHacktoberfest might have ended but the beauty of contributing to open source is that it s always the right time to start adiatiayu has some great tips to get you up to speed Contributing To Open Source Ayu Adiati・Oct ・ min read opensource hacktoberfest github codenewbie That s it for our weekly wrap up Keep an eye on dev to this week for daily content and discussions and if you miss anything we ll be sure to recap it next Tuesday	2021-11-02 22:01:20
Apple	AppleInsider - Frontpage News	Apple shipped estimated 6.5M MacBooks in Q3	https://appleinsider.com/articles/21/11/02/apple-shipped-estimated-65m-macbooks-in-q3?utm_medium=rss	Apple shipped estimated M MacBooks in QApple shipped an estimated million MacBooks in the third quarter of increasing the company s slice of the notebook market on the back of strong demand for products like MacBook Air According to the latest estimates from research firm Strategy Analytics MacBook shipments grew year over year to capture a share of the overall market The performance puts Apple in fourth place behind Lenovo HP and Dell Premium products like those offered by Apple gained momentum in the September quarter boosting consumer spend in part thanks to hefty discounts on popular models including MacBook Air Read more	2021-11-02 22:56:35
海外TECH	Engadget	University of Adelaide built a robot spider to scan Australia’s Naracoorte Caves	https://www.engadget.com/university-of-adelaide-cavex-spider-robot-224851184.html?src=rss	University of Adelaide built a robot spider to scan Australia s Naracoorte CavesIn the southeast of South Australia lie the Naracoorte Caves The national park is an UNESCO World Heritage Site known for its stalactites stalagmites and prehistoric fossils Recently a group of students from the University of Adelaide built a robot to complete a D scan of the site The project called CaveX saw the group create iterations of the model you see above before they settled on a final design They went with a robot that walks on a set of six legs out of a fear that one with treads or wheels would damage the surface of the caves The design also allowed it to traverse uneven terrain with a variety of different gaits Matthew KingAs for the D scans the hope is that they ll lead to new discoveries at the site quot We re looking at the cave surface to find new cave entrances which will hopefully lead to new fossil deposits quot Craig Williams one of the PhD students who worked on the project told ABC Australia quot That will help us enhance the range of knowledge on the fossils that are here quot The team that worked on the project hopes a new generation of engineering students will continue to work on the robot One day they d like to see it take advantage of technologies like computer vision AI to make it better at its job	2021-11-02 22:48:51
海外科学	NYT > Science	Biden Administration Moves to Limit Methane, a Potent Greenhouse Gas	https://www.nytimes.com/2021/11/02/climate/biden-methane-climate.html	biden	2021-11-02 22:44:04
海外科学	NYT > Science	Muriel Lezak, Leading Authority on Brain Injuries, Dies at 94	https://www.nytimes.com/2021/11/01/science/muriel-lezak-dead.html	brain	2021-11-02 22:03:48
金融	金融総合：経済レポート一覧	金融政策決定会合議事要旨（2021年9月21、22日開催分）	http://www3.keizaireport.com/report.php/RID/473701/?rss	日本銀行	2021-11-03 00:00:00
金融	金融総合：経済レポート一覧	FX Daily(11月1日)～ドル円、一時114円台半ばまで上昇	http://www3.keizaireport.com/report.php/RID/473706/?rss	fxdaily	2021-11-03 00:00:00
金融	金融総合：経済レポート一覧	日銀が世界の潮流に便乗する可能性：Market Flash	http://www3.keizaireport.com/report.php/RID/473708/?rss	marketflash	2021-11-03 00:00:00
金融	金融総合：経済レポート一覧	年金改革ウォッチ 2021年11月号～ポイント解説：2022年の改正点と総選挙後の課題：保険・年金フォーカス	http://www3.keizaireport.com/report.php/RID/473710/?rss	課題	2021-11-03 00:00:00
金融	金融総合：経済レポート一覧	米国政府によるStablecoinに対する規制方針：井上哲也のReview on Central Banking	http://www3.keizaireport.com/report.php/RID/473713/?rss	reviewoncentralbanking	2021-11-03 00:00:00
金融	金融総合：経済レポート一覧	東証一部企業の新市場区分選択～10月末経過レポート	http://www3.keizaireport.com/report.php/RID/473721/?rss	日本証券経済研究所	2021-11-03 00:00:00
金融	金融総合：経済レポート一覧	ヘッジファンド概況（2021年9月）～ヘッジファンドのパフォーマンス概況：当月のリターンは、9戦略中6戦略でマイナス	http://www3.keizaireport.com/report.php/RID/473724/?rss	日興リサーチセンター	2021-11-03 00:00:00
金融	金融総合：経済レポート一覧	2022年は二極化のなかの回復、「汽水域」を展望～足元の米中減速あっても腰折れせず：高田レポート	http://www3.keizaireport.com/report.php/RID/473738/?rss	岡三証券	2021-11-03 00:00:00
金融	金融総合：経済レポート一覧	銀行業界における次世代営業チャネルDX成功のポイント～プロジェクトアシュアランスの重要性	http://www3.keizaireport.com/report.php/RID/473743/?rss	pwcjapan	2021-11-03 00:00:00
金融	金融総合：経済レポート一覧	フロンティア（福証Q-Board）～自動車アフターパーツの企画・販売や電子玩具の受託製造を行う。今はファブレスだが自社生産を含めた供給体制の強化を目指す：アナリストレポート	http://www3.keizaireport.com/report.php/RID/473745/?rss	qboard	2021-11-03 00:00:00
金融	金融総合：経済レポート一覧	オープンな金融API連携が実現する、“未来の金融像”	http://www3.keizaireport.com/report.php/RID/473748/?rss	Detail Nothing	2021-11-03 00:00:00
金融	金融総合：経済レポート一覧	豪州での気候指数の動向～北米に続き、オーストラリアも気候変動を「見える化」：保険・年金フォーカス	http://www3.keizaireport.com/report.php/RID/473757/?rss	気候変動	2021-11-03 00:00:00
金融	金融総合：経済レポート一覧	各資産の利回りと為替取引によるプレミアム／コスト	http://www3.keizaireport.com/report.php/RID/473759/?rss	三菱ufj	2021-11-03 00:00:00
金融	金融総合：経済レポート一覧	豪中銀がイールドカーブ・コントロールを打ち切り～3年国債の利回り目標を撤廃：マーケットレポート	http://www3.keizaireport.com/report.php/RID/473760/?rss	三井住友トラスト	2021-11-03 00:00:00
金融	金融総合：経済レポート一覧	マーケットフォーカス（国内市場）2021年11月号～日経平均株価は、米長期金利の上昇や中国の景気減速懸念などから月初から4日連続の下落、一時27,500円台に下落	http://www3.keizaireport.com/report.php/RID/473761/?rss	三井住友トラスト	2021-11-03 00:00:00
金融	金融総合：経済レポート一覧	マンスリー・マーケット 2021年10月のマーケットをザックリご紹介～最近気になるトピック：インフレ懸念の高まりと円相場（対米ドル）。ピックアップカントリー：オーストラリア、メキシコ	http://www3.keizaireport.com/report.php/RID/473763/?rss	日興アセットマネジメント	2021-11-03 00:00:00
金融	金融総合：経済レポート一覧	オーストラリア マーケット動向（2021/11/2）～先週の豪ドルの対円レートは、上昇	http://www3.keizaireport.com/report.php/RID/473764/?rss	三井住友	2021-11-03 00:00:00
金融	金融総合：経済レポート一覧	先月のマーケットの振り返り（2021年10月）～10月の主要国の株式市場は、投資家のリスク選好姿勢が強まるなか、一部を除き堅調	http://www3.keizaireport.com/report.php/RID/473765/?rss	三井住友	2021-11-03 00:00:00
金融	金融総合：経済レポート一覧	実質無利子・無担保融資が信用金庫の決算に与えた影響の考察：Research Report	http://www3.keizaireport.com/report.php/RID/473772/?rss	researchreport	2021-11-03 00:00:00
金融	金融総合：経済レポート一覧	月刊インフラファンドレポート(2021年10月版)～10月の東証インフラファンド指数は、前月比-5.18ポイントの1,173.42ポイントで終了	http://www3.keizaireport.com/report.php/RID/473778/?rss	日本取引所グループ	2021-11-03 00:00:00
金融	金融総合：経済レポート一覧	【注目検索キーワード】デジタルシフト	http://search.keizaireport.com/search.php/-/keyword=デジタルシフト/?rss	検索キーワード	2021-11-03 00:00:00
金融	金融総合：経済レポート一覧	【お薦め書籍】グリーン・ジャイアント 脱炭素ビジネスが世界経済を動かす	https://www.amazon.co.jp/exec/obidos/ASIN/4166613278/keizaireport-22/	成長戦略	2021-11-03 00:00:00
ニュース	BBC News - Home	COP26: Biden attacks China and Russia leaders for missing summit	https://www.bbc.co.uk/news/world-59138578?at_medium=RSS&at_campaign=KARANGA	mistake	2021-11-02 22:35:44
ニュース	BBC News - Home	Cleo Smith: Missing 4-year-old found alive in Australia	https://www.bbc.co.uk/news/world-australia-59143494?at_medium=RSS&at_campaign=KARANGA	australian	2021-11-02 22:46:33
ニュース	BBC News - Home	Two dead after seven-storey fall at Abba tribute concert	https://www.bbc.co.uk/news/world-europe-59140788?at_medium=RSS&at_campaign=KARANGA	sweden	2021-11-02 22:33:20
ニュース	BBC News - Home	Covid-19: Sage scientist Sir Jeremy Farrar steps down from role	https://www.bbc.co.uk/news/uk-59143366?at_medium=RSS&at_campaign=KARANGA	advisory	2021-11-02 22:51:30
ニュース	BBC News - Home	Atalanta 2-2 Manchester United: Cristiano Ronaldo rescues visitors	https://www.bbc.co.uk/sport/football/59056853?at_medium=RSS&at_campaign=KARANGA	atalanta	2021-11-02 22:40:46
ニュース	BBC News - Home	Former All Blacks and Newcastle prop Hayman reveals early-onset dementia at 41	https://www.bbc.co.uk/sport/rugby-union/59142623?at_medium=RSS&at_campaign=KARANGA	dementia	2021-11-02 22:23:20
ビジネス	ダイヤモンド・オンライン - 新着記事	丸紅(9810)、「増配」を発表し、配当利回り5.0％に アップ！ 配当額は1年で1.5倍に増加、2022年3月期 は前期比で「18円」増となる「1株あたり51円」に！ - 配当【増配・減配】最新ニュース！	https://diamond.jp/articles/-/286560	丸紅、「増配」を発表し、配当利回りにアップ配当額は年で倍に増加、年月期は前期比で「円」増となる「株あたり円」に配当【増配・減配】最新ニュース丸紅が、年月期の配当予想の修正増配を発表し、配当利回りがに丸紅は、年月期の年間配当を前回予想比で「円」の増配、前期比では「円」の増配となる「株あたり円」に修正すると発表した。	2021-11-03 07:30:00
LifeHuck	ライフハッカー［日本版］	数量限定！アウトドア環境で頼もしい電動アシスト自転車「XPLORER」	https://www.lifehacker.jp/2021/11/245189-machi-ya-xplorer_start.html	xplorer	2021-11-03 08:00:00
北海道	北海道新聞	客引き禁止、札幌市が新条例　居酒屋・カラオケ店も対象、来春にも	https://www.hokkaido-np.co.jp/article/607441/	業種	2021-11-03 07:18:03
北海道	北海道新聞	ＮＹ円、１１４円近辺	https://www.hokkaido-np.co.jp/article/607459/	外国為替市場	2021-11-03 07:10:00
北海道	北海道新聞	５～１１歳への接種推奨　米ＣＤＣ有識者委、開始へ準備	https://www.hokkaido-np.co.jp/article/607460/	疾病対策センター	2021-11-03 07:10:00