投稿時間:2023-01-06 21:31:14 RSSフィード2023-01-06 21:00 分まとめ(35件)

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python	Pythonタグが付けられた新着投稿 - Qiita	Pythonで一次元有限要素法(FEM)を実装する	https://qiita.com/kakeru4017/items/e4a677643820a8e94d05	leftauright	2023-01-06 20:55:36
js	JavaScriptタグが付けられた新着投稿 - Qiita	React 18 + Javascript版Amplify v5環境で手動configureする方法	https://qiita.com/namakenia/items/75b279f819f36d298b01	amplif	2023-01-06 20:08:40
AWS	AWSタグが付けられた新着投稿 - Qiita	React 18 + Javascript版Amplify v5環境で手動configureする方法	https://qiita.com/namakenia/items/75b279f819f36d298b01	amplif	2023-01-06 20:08:40
技術ブログ	Developers.IO	AWS SAM テンプレートに既存リソースをインポートする手順を確認する	https://dev.classmethod.jp/articles/aws-sam-template-import-resources/	awscloudformation	2023-01-06 11:51:45
海外TECH	MakeUseOf	Best 8 HTML and CSS Projects for Beginners	https://www.makeuseof.com/html-css-projects-beginners/	beginnerslooking	2023-01-06 11:30:15
海外TECH	MakeUseOf	9 Questions About ChatGPT, Answered	https://www.makeuseof.com/chatgpt-questions-and-answers/	chatgpt	2023-01-06 11:15:16
海外TECH	MakeUseOf	Will Qi2 Bring Apple's MagSafe Wireless Charging Tech to Android Devices?	https://www.makeuseof.com/qi2-magsafe-android/	Will Qi Bring Apple x s MagSafe Wireless Charging Tech to Android Devices The new Qi wireless charging standard is built on Apple s MagSafe tech and could bring safer and faster charging to Android phones soon	2023-01-06 11:05:15
海外TECH	MakeUseOf	5 Reasons Why You Don't Need an Expensive Phone	https://www.makeuseof.com/reasons-you-dont-need-expensive-phone/	devices	2023-01-06 11:01:15
海外TECH	DEV Community	Awesome Readme Examples for Writing better Readmes	https://dev.to/documatic/awesome-readme-examples-for-writing-better-readmes-3eh3	Awesome Readme Examples for Writing better Readmes Awesome Readme Examples for Writing better Readmes IntroductionWriting the readme should be your priority after completing the code But writing a good readme requires some points to cover while writing a Readme I have written a piece about How to Write an Awesome Readme discussing readme how to write it and tools that can assist in this You can check the discussion section for more insightful stuff The only thing that I missed in the article was to mention some good readme examples So in this article I am going to list down some awesome remade that will help you in taking inspiration for writing your reamde So let s get started athityakumar colorlsIt is a Ruby script to colorize the ls output with color and icons The readme looks amazing It starts with the name of the project and then the badges At the top we can see a short description of the project with an image to illustrate the idea better It covers most of the relevant topics that can be included in the readme It covers a Table of Content Installation guide Contributing License and other important stuff The usage of images is very well illustrated in the readme as I show images of their every feature This can easily be used as a template for small projects surajondev medusa discord integrationIt is one of the repositories that I created for submitting a project for a hackathon It can easily be used as a template for submitting projects for a hackathon I have covered topics such as description preview participation details installation guide reference resources and others It starts with the name of the project and then a beautiful cover image I have shown the output of the project by displaying images You can also use gifs for output public apis public apisThis is a repository that contains numerous APIs that can be used to build an application The feature of tables in markdown is well used by such repositories At the top we have the link name and sub title explaining the repository You can also find all the useful links such as contributing guides Issues and licenses at the top of the project This kind of list is very huge That s having a good structure and a table of content becomes important This repository has both of them After every category of API we have Back to Index link to get back to the table of content forem foremA repository containing awesome resources for React ecosystem The contains most of the required things that you want to see in a readme It has links to other files too for further explanation on the topic The readme is not long but still they have a table of content That show their efforts for making it even better They have covered the topics such as Contributing Getting started developer documentation license and others I contributed to their repository and the guide helped me a lot in setting up the project refinedev refineRefine is an open source headless web application framework The readme file looks beautiful with images badges and emojis that they have included in it At the top the name of the project with a short description They have added all the useful links at the top for convenience It addresses all the information that is related to the project They have covered all the important topics such as setting up projects contributions licenses and others ConclusionThis repository is an example of a great reamde structure You can use them as a template to write your own reamde according to your requirement I hope that the article has helped you in providing some awesome readme to inspire your next project s readme Thanks for reading the article	2023-01-06 11:30:00
海外TECH	DEV Community	A Beginner's Guide to Feature Selection in Machine Learning: Techniques and Tips	https://dev.to/phylis/a-beginners-guide-to-feature-selection-in-machine-learning-techniques-and-tips-459o	A Beginner x s Guide to Feature Selection in Machine Learning Techniques and Tips Introduction to Feature Selection As a data scientist one of the crucial steps in the machine learning process is selecting the most relevant and important features to train our models on This is because using too many irrelevant or redundant features can lead to overfitting and poor model performance In this blog we will explore various techniques and approaches for selecting the best features for your machine learning models What is Feature selection Feature selection also known as variable selection or attribute selection is the process of identifying and selecting a subset of relevant features from a larger set of features for use in a machine learning model This is often done to improve the model s performance and reduce the complexity of the model Need for feature selection Reduce over fitting overfitting is a situation where your model is too complex for your data To improve performance of a machine learning model it is important to correctly select features to make it simpler thus reducing the risk of overfitting Improve accuracy of a modelReducing the input variable into your model by selecting the most critical variables and eliminating irrelevant features increases the prediction power of algorithms Simplify models for easy interpretationDiscarding the variables with less significant values enables easy interpretation of variables Increase training speed Reducing data features reduces algorithm complexity and algorithm trains faster Feature selection techniquesThere are various feature selection techniques including Univariate Feature selectionMultivariate Feature selection Univariate Feature SelectionDescription It involves manually visiting every feature and checking itsimportance against a target It is useful when dealing with few features Techniques use of personal judgement Not Recommended ifyou are not an expert Computing Variance or standard deviation Computing Correlation i e Pearson Most Recommended Implementing Univariate Feature selection using Python Import librariesimport pandas as pdimport numpy as npcreate a dummy data set and load it into a Pandas DataFrame Creating dummy data set with the following attributes size of house sq m number of bedrooms number of parking slots montly rent usd data Size Bedrooms Parking Rent Create a DataFramedf pd DataFrame data dfOutputselect a target variable and feature matrix Choosing target variable and feature matrixX df drop Rent Feature Matrixy df Rent Target VariableXOutputSet a variance threshold Target Variable Rent Feature Matrix Size and Bedrooms Threshold Variance Value comparisonsvar df Rent Size var axis var df Rent Bedrooms var axis var df Rent Parking var axis var df Rent Rent var axis print var print print var print print var print varvarvar mean var mode var mode var mode In the example above we use a dummy data set that has the attributes Size Bedrooms Parking Rent Rent is chosen to the target variable and Size Bedrooms Parking form the feature matrix Each feature in the feature matrix is then compared against the target variable to check its importance Variance is computed for each comparison The minimum variance threshold used in this example is that is if the variance of the comparison is less than then the affected samples have almost the same values This implies that these samples will not generate any meaningful information when applied on a predictive model In this example we see that feature Size when compared to the target variable Rent yields variance that are less than for all the samples This feature can be ignored because it will not bring any predictive power to the model Multivariate Feature selection It is the most powerful method when carrying out machine learning and data analytics tasks Multivariate feature selection involves identifying and selecting the most important features in a dataset based on their ability to explain the variance in the target variable This is useful when dealing with large datasets as it helps to reduce the complexity of the data and improve the efficiency of the modeling process They are different approaches to multivariate feature selection and how they can be applied in various machine learning contexts Filter methodsThese methods use statistical measures to evaluate the relevance of each feature to the target variable and select the most relevant ones Examples include chi squared test information gain and correlation coefficient These methods can be applied in any machine learning context where there is a target variable to predict Wrapper methodsThese methods use a machine learning model as a wrapper to evaluate the importance of each feature The model is trained using a subset of features and the performance is evaluated The subset of features is then modified and the process is repeated until the best performing subset is found Examples include recursive feature elimination backward selection and forward selection These methods are more computationally expensive and are best suited for smaller datasets Embedded methodsThese methods use the structure of the machine learning model itself to identify the most important features For example in a decision tree model the features that are most important for making decisions are considered the most important Examples include lasso regression Elastic Net regularization ridge regularization Random forest Gradient boosting Machin and decision trees These methods can be applied in any machine learning context where the model has a built in feature selection method Linear modelsIn this method the relationship between each feature and the dependent variable is evaluated using a linear model The features that have the highest coefficients in the model are selected Example We will implement the use of multivariate feature selection using California Housing dataset as follows import the necessary libraries and load in the dataset import pandas as pdfrom sklearn model selection import train test splitfrom sklearn preprocessing import StandardScalerfrom sklearn feature selection import SelectKBest f regressionfrom sklearn linear model import LinearRegressionLoad in the California Housing dataset data pd read csv housing csv split the data into features X and target y X data drop median house value axis y data median house value Split the data into training and test sets and scale the features using StandardScaler split the data into training and test setsX train X test y train y test train test split X y test size scale the features using StandardScalerscaler StandardScaler X train scaler fit transform X train X test scaler transform X test Use the SelectKBest function to select the top features based on their F values from the f regression function select the top features using SelectKBestselector SelectKBest f regression k X train selected selector fit transform X train y train X test selected selector transform X test print the selected featuresprint X columns selector get support Finally we can fit a Linear Regression model using the selected features and evaluate its performance fit a Linear Regression model using the selected featuresmodel LinearRegression model fit X train selected y train evaluate the model on the test setprint Test set R score f format model score X test selected y test ConclusionIn conclusion feature selection is an important step in the machine learning process as it helps to improve the performance of a model and reduce the complexity of the data By carefully selecting the most relevant and informative features we can build more accurate and efficient models that are better able to make predictions on new data While there are various methods available for feature selection it is important to consider the specific needs of your dataset and choose the method that best fits your goals Ultimately the right feature selection technique can greatly enhance the performance of your machine learning model and lead to more successful results	2023-01-06 11:09:44
Apple	AppleInsider - Frontpage News	New Apple Card users can get a 'free' Wall Street Journal subscription	https://appleinsider.com/articles/23/01/06/new-apple-card-users-can-get-a-free-wall-street-journal-subscription?utm_medium=rss	New Apple Card users can get a x free x Wall Street Journal subscriptionUnder an Apple Card promotion limited to new users users who apply for the card and subscribe to the Wall Street Journal digitally for a year can have the fee paid back by Apple Apple has previously offered promotions for all Apple Card users and incentives like increased cashback for new subscribers It s latest offer is limited to new Apple Card applicants with the company offering to pay Daily Cash if a new user subscribes to the Wall Street Journal So it s not that users can subscribe via the Apple Card or perhaps Apple News and get a year s free subscription They have to get the Apple Card then take out a subscription via specific promotion page and then they will get the money ーafter the first month Read more	2023-01-06 11:42:29
Apple	AppleInsider - Frontpage News	Samsung profits plummet 69% on falling demand for processors	https://appleinsider.com/articles/23/01/06/samsung-profits-plummet-69-on-falling-demand-for-processors?utm_medium=rss	Samsung profits plummet on falling demand for processorsSamsung has reported an eight year low for its operating profit in its Q earnings results dropping to billion from billion in the same period a year before One reason for the steep decrease in profit is how Samsung supplies many iPhone displays but Apple has been hit by production delays in China Then there are also ongoing US sanctions against China which are affecting many technology firms However according to the Financial Times Samsung s results are because it has stepped up chip production even though the market has declined Samsung reportedly increased memory chip production by in Q for instance Read more	2023-01-06 11:17:25
Apple	AppleInsider - Frontpage News	ShiftCam SnapGrip review: Better iPhone photos with MagSafe	https://appleinsider.com/articles/23/01/06/shiftcam-snapgrip-review-better-iphone-photos-with-magsafe?utm_medium=rss	ShiftCam SnapGrip review Better iPhone photos with MagSafeApple introduced the MagSafe accessory ecosystem as a new charging solution for iPhone but the ShiftCam SnapGrip evolves the concept with an interesting click on photography tool ShiftCam SnapGrip uses MagSafe to attach to the iPhoneYour iPhone might be the best camera you own or at the least the most portable However shooting images can be somewhat cumbersome with its slick soap bar design and fiddly touchscreen shutter Read more	2023-01-06 11:02:58
海外TECH	Engadget	Tesla brings back the option of a round steering wheel for the Model S and X	https://www.engadget.com/tesla-is-now-offering-a-round-steering-wheel-option-on-model-s-and-x-113941070.html?src=rss	Tesla brings back the option of a round steering wheel for the Model S and XWhen Tesla started offering a yoke steering wheel in Model X and Model S EVs CEO Elon Musk rejected the idea of a more conventional option Now the company has indeed brought back the round steering wheel as an option in the configurator Electrek has reported Selecting it doesn t appear to change the price or delivery time If you already bought a Tesla Model S or X with a yoke and don t like it you have options too As pointed out by YouTuber and Tesla owner Marques Brownlee the company is now offering a option to replace the yoke with a round steering wheel quot This retrofit is only compatible with Model S and Model X vehicles configured with a Yoke Steering Wheel Purchase price includes installation through Tesla Service quot Tesla notes It adds that the wheel has quot no stalks or shifters quot The yoke divided Tesla fans as many felt it wasn t practical for one handed use and that it was awkward when driving at slow speeds It was also deemed potentially unsafe by some owners in case you needed to do quick maneuvers like a skid recovery Yet another round wheel is boring amp blocks the screen FSD in panoramic mode looks way better with a yoke ーElon Musk elonmusk September When the yoke came along Tesla also replaced all the stalks with force touch buttons another thing some drivers weren t crazy about As mentioned though the new wheel option doesn t have any stalks or shifters either and the design appears to be based on the yoke in terms of spoke positioning buttons etc nbsp When asked in a tweet in if Tesla would offer a round steering wheel as an option to the yoke he replied simply quot No quot He went on to explain that quot yet another round wheel is boring amp blocks the screen FSD in panoramic mode looks way better with a yoke quot Apparently there were enough potential buyers who felt otherwise	2023-01-06 11:39:41
海外TECH	CodeProject Latest Articles	Injecting Service Dependencies to Entities with Entity Framework Core 7.0	https://www.codeproject.com/Articles/5351405/Injecting-Service-Dependencies-to-Entities-with-En	entity	2023-01-06 11:27:00
海外TECH	CodeProject Latest Articles	Build Your Own Video Streaming Server with Node.js and Koa	https://www.codeproject.com/Articles/5350209/Build-Your-Own-Video-Streaming-Server-with-Node-js	build	2023-01-06 11:18:00
医療系	医療介護 CBnews	医師時短計画の評価申請「遅くても夏前に」-勤務環境評価センター、早めの対応呼び掛け	https://www.cbnews.jp/news/entry/20230106201128	医療機関	2023-01-06 20:40:00
医療系	医療介護 CBnews	セキュリティや災害対策を重視しシステム構築支援-藤田医科大のPHR基盤でAWS遠山マネージャー	https://www.cbnews.jp/news/entry/20230106201324	awsamazonwebservices	2023-01-06 20:35:00
海外ニュース	Japan Times latest articles	Yen rally under threat after break of long-term support level	https://www.japantimes.co.jp/news/2023/01/06/business/financial-markets/bank-of-japan-yen-dollar/	Yen rally under threat after break of long term support levelThe currency fell for a fourth day Friday following stronger than forecast U S employment data dropping further below a support line starting from October s three decade low	2023-01-06 20:33:22
ニュース	BBC News - Home	Harry's talk of Taliban kills in Afghanistan tarnishes reputation - ex-commander	https://www.bbc.co.uk/news/uk-64185176?at_medium=RSS&at_campaign=KARANGA	chess	2023-01-06 11:34:36
ニュース	BBC News - Home	Gianluca Vialli: Former Chelsea, Juventus and Italy striker dies aged 58	https://www.bbc.co.uk/sport/football/64039302?at_medium=RSS&at_campaign=KARANGA	gianluca	2023-01-06 11:53:06
ニュース	BBC News - Home	Census data reveals LGBT+ populations for first time	https://www.bbc.co.uk/news/uk-64184736?at_medium=RSS&at_campaign=KARANGA	wales	2023-01-06 11:46:18
ニュース	BBC News - Home	House prices drop for fourth month in a row	https://www.bbc.co.uk/news/business-64183817?at_medium=RSS&at_campaign=KARANGA	halifax	2023-01-06 11:55:58
ニュース	BBC News - Home	Katharine Birbalsingh: Head teacher quits as social mobility adviser	https://www.bbc.co.uk/news/uk-politics-64187298?at_medium=RSS&at_campaign=KARANGA	birbalsingh	2023-01-06 11:34:23
ニュース	BBC News - Home	Edwin Chiloba: LGBTQ activist found dead in Kenya	https://www.bbc.co.uk/news/world-africa-64184372?at_medium=RSS&at_campaign=KARANGA	designer	2023-01-06 11:52:13
ニュース	BBC News - Home	Gregg Berhalter: US coach wants to stay in role amid investigation into kicking his wife	https://www.bbc.co.uk/sport/football/64184631?at_medium=RSS&at_campaign=KARANGA	Gregg Berhalter US coach wants to stay in role amid investigation into kicking his wifeGregg Berhalter wants to continue as US men s head coach but feels saddened by the emergence of revelations he kicked his wife during an argument in when they were teenagers	2023-01-06 11:37:09
北海道	北海道新聞	「国際リゾートとして成長」　倶知安町長、新年交礼会で抱負	https://www.hokkaido-np.co.jp/article/784322/	倶知安町	2023-01-06 20:38:00
北海道	北海道新聞	首相訪問直前、伊勢神宮で爆竹破裂	https://www.hokkaido-np.co.jp/article/784321/	三重県警	2023-01-06 20:37:00
北海道	北海道新聞	経営不安払拭へ支援体制を強化　余市商工会議所会頭	https://www.hokkaido-np.co.jp/article/784320/	余市商工会議所	2023-01-06 20:36:00
北海道	北海道新聞	ロシア軍、一方的停戦入り　「複数回攻撃」主張も	https://www.hokkaido-np.co.jp/article/784295/	複数回	2023-01-06 20:38:08
北海道	北海道新聞	倶知安町、リゾート開発に新ルール案　宿泊施設の延べ床面積制限　森林景観へ配慮も	https://www.hokkaido-np.co.jp/article/784267/	倶知安町	2023-01-06 20:33:00
北海道	北海道新聞	高校バレー、男子の鎮西など４強　前回Ｖの日本航空敗退	https://www.hokkaido-np.co.jp/article/784299/	日本航空	2023-01-06 20:12:56
北海道	北海道新聞	神村学園、高校サッカー準決勝へ　「自信しかない」と福田	https://www.hokkaido-np.co.jp/article/784309/	神村学園	2023-01-06 20:12:56
北海道	北海道新聞	Ｇ７札幌会合で「成果文書を」　西村環境相が意欲	https://www.hokkaido-np.co.jp/article/784311/	西村明宏	2023-01-06 20:18:00
北海道	北海道新聞	日米２プラス２、反撃力協議　中国にらみ対処方針強化	https://www.hokkaido-np.co.jp/article/784308/	安全保障	2023-01-06 20:06:00