投稿時間:2022-04-22 12:37:00 RSSフィード2022-04-22 12:00 分まとめ(44件)

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IT	InfoQ	AWS Firewall ManagerがPalo Alto NetworksのCloud Next Generation Firewallに対応	https://www.infoq.com/jp/news/2022/04/aws-palo-alto-firewall/?utm_campaign=infoq_content&utm_source=infoq&utm_medium=feed&utm_term=global	AWSFirewallManagerがPaloAltoNetworksのCloudNextGenerationFirewallに対応AWSは先頃、FirewallManagerがPaloAltoNetworksのCloudNextGenerationFilewallsNGFWをサポートすることを発表した。	2022-04-22 02:18:00
IT	InfoQ	BBCの新たなサーバーレスプラットフォームによりスケーラビリティとパフォーマンスが向上	https://www.infoq.com/jp/news/2022/04/bbc-one-year-serverless/?utm_campaign=infoq_content&utm_source=infoq&utm_medium=feed&utm_term=global	BBCの新たなサーバーレスプラットフォームによりスケーラビリティとパフォーマンスが向上新しいWebCoreサーバーレスプラットフォームへの移行から年後、BBCは、アーキテクチャのメリットを享受し始めた。	2022-04-22 02:14:00
IT	InfoQ	React 18で並列レンダラーを導入	https://www.infoq.com/jp/news/2022/04/react-18-concurrent-renderer/?utm_campaign=infoq_content&utm_source=infoq&utm_medium=feed&utm_term=global	byguyneshertranslatedby	2022-04-22 02:11:00
IT	InfoQ	Amazon EKSがKubernetes 1.22のサポートを発表	https://www.infoq.com/jp/news/2022/04/eks-kubernetes-122/?utm_campaign=infoq_content&utm_source=infoq&utm_medium=feed&utm_term=global	AmazonEKSがKubernetesのサポートを発表AmazonElasticKubernetesServiceEKSチームは、Kubernetesのサポートを発表した。	2022-04-22 02:09:00
IT	InfoQ	ソフトウェアが気候変動にどのように影響するか、ソフトウェアエンジニアがそれに対して何ができるか	https://www.infoq.com/jp/news/2022/04/climate-change-software-engineer/?utm_campaign=infoq_content&utm_source=infoq&utm_medium=feed&utm_term=global	ソフトウェアが気候変動にどのように影響するか、ソフトウェアエンジニアがそれに対して何ができるか地球上のいたるところで大量のソフトウェアが実行されており、このソフトウェアは実行時にエネルギーを消費する。	2022-04-22 02:07:00
ROBOT	ロボスタ	ファミリーマートとLuupが資本業務提携　ファミマ店舗に電動キックボードのポート設置を加速　免許不要の改正道交法の成立後の発表	https://robotstart.info/2022/04/22/famima-luup.html	ファミリーマートとLuupが資本業務提携ファミマ店舗に電動キックボードのポート設置を加速免許不要の改正道交法の成立後の発表シェアツイートはてブファミリーマートは株式会社Luupとの資本業務提携契約を締結したことを発表した。	2022-04-22 02:28:40
IT	ITmedia 総合記事一覧	[ITmedia ビジネスオンライン] 「Webサイトが使いやすい地方銀行」ランキング　3位「横浜銀行」、2位「群馬銀行」、1位は？	https://www.itmedia.co.jp/business/articles/2204/22/news100.html	伊予銀行	2022-04-22 11:50:00
IT	ITmedia 総合記事一覧	[ITmedia ビジネスオンライン] 23年卒学生が内定を得た業界　「建設・住宅・不動産」「調査・コンサルタント」を抜いた1位は？	https://www.itmedia.co.jp/business/articles/2204/22/news093.html	itmedia	2022-04-22 11:47:00
IT	ITmedia 総合記事一覧	[ITmedia News] オバマ元米大統領、「SNSは米国の二極化に影響を及ぼした」と語り対策を提示	https://www.itmedia.co.jp/news/articles/2204/22/news106.html	itmedia	2022-04-22 11:42:00
IT	ITmedia 総合記事一覧	[ITmedia ビジネスオンライン] 華麗な経歴から一転、年収は4分の1に　“壁しかない”中でラクスル永見CFOが見つけた「成功したCFOの共通点」とは？	https://www.itmedia.co.jp/business/articles/2204/22/news064.html	itmedia	2022-04-22 11:40:00
IT	ITmedia 総合記事一覧	[ITmedia News] 無料でラーメン食べ放題、麺特化のEC事業者が福利厚生で　金額に上限なし	https://www.itmedia.co.jp/news/articles/2204/22/news057.html	itmedia	2022-04-22 11:30:00
IT	ITmedia 総合記事一覧	[ITmedia ビジネスオンライン] 「上場時に、うちの株を全部売ってほしい」──“IPO革命”は、なぜ実現できた？　ラクスル永見CFOに聞く	https://www.itmedia.co.jp/business/articles/2204/22/news062.html	itmedia	2022-04-22 11:30:00
IT	ITmedia 総合記事一覧	[ITmedia News] こと座流星群は23日未明ピーク　数は少ないが明るい流れ星　広い地域で天候「バッチリ」、ライブ配信も	https://www.itmedia.co.jp/news/articles/2204/22/news102.html	itmedia	2022-04-22 11:21:00
python	Pythonタグが付けられた新着投稿 - Qiita	地獄のフィボナッチ数列@Python	https://qiita.com/Ytz_Ichi/items/def7bccb7f5972f9d53c	誰か	2022-04-22 11:05:59
js	JavaScriptタグが付けられた新着投稿 - Qiita	JavaScriptで配列の要素を入れ替える方法	https://qiita.com/shimajiri/items/1d6663848bd47b82e2fe	econsoleloghogehogehogeho	2022-04-22 11:51:32
技術ブログ	Developers.IO	【CloudFormation】一撃でプライベートサブネットにEC2を起動し、SSMポートフォワード経由でRDPする	https://dev.classmethod.jp/articles/cloudformation-private-ssmrdp/	cloudf	2022-04-22 02:49:25
技術ブログ	クックパッド開発者ブログ	クックパッドマート最難解ロジック!?「採番」	https://techlife.cookpad.com/entry/cookpad-mart-assignments-2022	難解	2022-04-22 11:23:52
海外TECH	DEV Community	Getting Page Titles for URLs with Ruby	https://dev.to/bphogan/getting-page-titles-for-urls-with-ruby-ndn	Getting Page Titles for URLs with RubyIf you have a URL and you d like to get the title for the page you ll need to fetch the URL parse the source and grab the text from the lt title gt tag If you have a large number of URLs you ll want to automate this process You can do this with a few lines of Ruby and the Nokogiri library In this tutorial you ll create a small Ruby program to fetch the title for a web page Then you ll modify the program to work with an external file of URLs Finally you ll make it more performant by using threads Fetching the Title from the URLThe Nokogiri library lets you use CSS selector syntax to grab nodes from HTML and the URI library lets you quickly read a file from a URL like the cURL command does To do this install the nokogiri and uri gems gem install urigem install nokogiriCreate a Ruby script called get titles rb and add the following code to load the libraries open a URL as a file send its contents to Nokogiri and extract the value of the lt title gt tag require nokogiri require open uri url URI open url do f doc Nokogiri HTML f title doc at css title text puts titleendSave the file and run the program ruby get titles rbThe result shows the page title for Google GoogleTo do this for multiple URLs put the URLs in an array manually or get them from a file Reading URLs from a FileYou may already have the list of URLs in a file which may have come from a data export Using Ruby s File readlines you can quickly convert the file into an array Create a new file called links txt and add a couple of links Make sure one of them is a bad URL you ll make sure to handle errors https devtoSave the file Now return to your get titles rb file and modify the code so it reads the file in line by line and uses each line as a URL get titles rbrequire nokogiri require open uri lines File readlines links txt lines each do line url line chop URI open url do f doc Nokogiri HTML f title doc at css title text puts title endrescue SocketError puts url can t connect Bad URL endEach line from the file will have a line break at the end which you remove with the chop method before storing the value in the url variable The URI open method will throw a SocketError if it can t connect and so you rescue that error with a sensible message Save the file and run the program again ruby get titles rbThis time you see Google s page title for the first URL and the error message for the second Googlehttps devto Can t connect Bad URL This version isn t the fastest when your list gets large On a file with URLs the process took minutes A lot of the time was the network latency Each request takes some time to resolve and get the results Let s make it faster Processing URLs ConcurrentlyTo make this process more efficient and much faster you ll need to use threads And if you use threads you ll need to think about thread pooling because if you use too many threads you ll run out of resources The concurrent ruby gem makes this much less complex by giving you promises in Ruby which have their own pooling mechanism Install the concurrent ruby gem gem install concurrent rubyTo use it you ll create a job for each line in the file Each job is a promise which takes a block containing the code you want to execute Then following the loop you collect all of the promises and call the value method which blocks until the promise is complete The pattern looks like this Create a job for each linejobs lines map do line Concurrent Promises future do do the work endend get all the jobs blocking until they all finish Concurrent Promises zip jobs value Modify the program to include the concurrent library and create a promise for each URL read Then get the results get titles rbrequire nokogiri require open uri require concurrent lines File readlines links txt jobs lines map do line Concurrent Promises future do url line chop URI open url do f doc Nokogiri HTML f title doc at css title text puts title end rescue SocketError puts url can t connect Bad URL endendConcurrent Promises zip jobs valueIn this version of the program you re printing the results to the screen But you could return a value instead and print those This time a file with URLs took around seconds to process That s a significant speed improvement and demonstrates why concurrent processing is important for these kinds of tasks ConclusionIn this tutorial you used Ruby to get page titles from URLs and you then optimized it using the concurrent ruby library to take advantage of threads and thread pooling To keep exploring read in the data from a CSV file and use the program to generate a new CSV file with the URL and the title in separate columns Then see if you can pull additional information out of the URLs such as the lt meta gt descriptions Like this post Support my writing by purchasing one of my books about software development	2022-04-22 02:21:43
海外TECH	DEV Community	What is Azure Synapse Analytics?	https://dev.to/integerman/what-is-azure-synapse-analytics-32n9	What is Azure Synapse Analytics Recently I had the opportunity to spend some time studying Azure Synapse Analytics and I wanted to share a high level summary of what I learned so you can determine if it is a good match for your data processing needs This content is also available in video form on YouTube Introducing Synapse AnalyticsIn Microsoft rebranded their existing Azure SQL Data Warehouses product to Azure Synapse Analytics and began overhauling its features The following year Microsoft unveiled their “workspaces feature set featuring dedicated SQL pools and many more of the features that I ll cover in this article At its core Synapse Analytics takes a number of existing parts of Azure and wraps them together under a single unified banner and backs it with a powerful and scalable data processing engine Azure Synapse Analytics gives us a single unified product that can repeatably ingest data from data lakes data warehouses relational databases and non relational databases Synapse Analytics can then transform and store this data using the same core pipeline features and code from Azure Data Factory but it does it using distributed processing technologies with Spark pools and SQL pools Synapse Analytics lets us consume this data either by storing in a data warehouse or by providing easy ways of integrating with Power BI for analysis and Azure Machine Learning for machine learning model generation This lets Azure Synapse Analytics serve as an ETL ELT and batch processing pipeline for large scale data sources and support real time analytics needs And Synapse Analytics does all of this in a way that is easy for enterprises to secure and monitor What about Azure Data Factory To some of you this may sound a little familiar to Azure Data Factory so let s talk briefly about the difference between the two Azure Data Factory lets users define their own extract transform load and extract load transform pipelines that consist of a wide variety of activities This effectively lets teams build custom solutions for data engineering in the cloud Now Azure Data Factory does have some limitations however First of all while you can integrate a lot of things into it the process for doing so is not very centralized Additionally it s hard for an organization to get an accurate glimpse into all aspects of their service offering or secure things in a unified way This effectively makes Azure Data Factory powerful but limited in its applications for the enterprise Azure Synapse Analytics is designed to address this gap while providing additional features and capabilities Ingesting DataIn Synapse Analytics you can define any number of data sources to feed into your Synapse workspace These sources could be SQL databases like Azure SQL or Postgres or they could be NoSQL databases such as CosmosDB Synapse Analytics also supports blob file and table storage as well as data lakes and Spark based data systems One thing to highlight here is that data can also be ingested from outside of Azure via connectors to other cloud providers or even by installing an Integration Runtime on premises to provide self hosted data to Synapse Analytics in a hybrid cloud environment Once you ve configured data sources they re now available to be copied into your Azure Synapse Analytics data lake Copy Data ToolMicrosoft gives us a very robust Copy Data Tool that allows you to copy data from your data sources into your data lake This copy operation can be run manually can be given a loose schedule or be run on a tumbling window for recurring intervals like weekly tasks Mapping Data FlowsSynapse also lets you define something called a Mapping Data Flow that allows you to aggregate filter sort and otherwise transform the data you re loading You can even merge together multiple data sources via join operations If you ve encountered Mapping Data Flows before in Azure Data Factory Azure Synapse Analytics actually shares a lot of the same underlying code so you should be familiar with many of the features PipelinesLet s talk about transforming data Synapse allows you to define pipelines that can trigger mapping data flow and data copy tasks Pipelines has over different activities for data processing and transformation including the ability to run custom code in Spark or Databricks notebooks ScalabilityLet s talk about the scalability factors of Synapse because this is where Synapse Analytics really shines One of the reasons that Synapse is so scalable is because it relies on two separate but related pools a SQL pool for querying and a Spark pool for data processing Synapse uses SQL pools to query its data in a very scalable manner In a SQL pool there is a single control node which receives and distributes queries to individual compute nodes Compute nodes are responsible for executing queries against data storage and returning results to the control node There are two different SQL pool models available Dedicated SQL pools and Serverless SQL pools Dedicated SQL PoolsDedicated SQL pools are closer to a traditional database model where you are paying for dedicated resources based on how many compute nodes you need When a query comes in the control node figures out which compute nodes have relevant data and asks them to run specific queries against their data This data is then returned to the control node Compute nodes can be run in round robin mode where data is distributed evenly in hashed mode where different data values go to different compute nodes or in replicated mode where the same data is stored on every compute node Each of these modes have different scalability and performance characteristics Round robin is the default and has average overall performance characteristics Hashed mode performs well for larger data stores but does lead to imbalanced loads across compute nodes Replicated mode is best for small data stores that need very high performance at the expense of duplicating data across all nodes Serverless SQL PoolsDedicated SQL pools are great for high performance and high volume data processing but they can be a bit too expensive for many smaller workflows Serverless SQL pools address this by operating in a different model Instead of declaring the number of compute nodes you want up front Azure stores your data outside of its compute nodes When you need to run a query the control node analyzes the complexity of the query and sends it to an appropriate number of compute nodes from Azure s available compute nodes for the entire region These compute nodes then access your stored data Serverless SQL Pools aren t as fast as a dedicated pool but they are usually cheaper for scenarios where you are infrequently querying your data store Spark PoolsIf SQL Pools are what we use to query our data Spark Pools are what we use to work with the results Synapse Spark is Microsoft s variant on Apache Spark that is purpose built for Synapse Analytics and can run dedicated notebooks to run custom code against data in your data lake Like SQL Pools Spark Pools follow a distributed model where a single Driver node hands off tasks to executors who then run those tasks in slots in the various cores they have available This allows Spark Pools to dynamically scale up and down to meet your analytical needs or even shut down entirely when not used Spark notebooks can run code in a number of languages Spark SQLPythonC ScalaNotably Spark notebooks cannot run any R code as of this moment Consuming Data via IntegrationsOnce data is loaded it can be sent to a number of services outside of Synapse Analytics Most interestingly to me you can use Synapse to trigger Azure Machine Learning pipelines and even generate Azure Automated ML code in notebooks from existing datasets On the data analysis side of things Power BI can be linked to Synapse Analytics to provide a dedicated visualization tool allowing users to report on data in the data lake or create dashboards for key stakeholders Azure Data Explorer is an interesting data exploration and visualization service that seems to be rapidly growing at the moment and there are early signs of promising integrations between the two services as well Synapse Analytics as your Enterprise Data PipelineClearly we can do a lot with Synapse Analytics but it has some solid enterprise capabilities to mention as well Synapse Analytics gives us a dedicated central pane to monitor and manage all aspects of the data warehouse and processing pipeline including both our SQL pools and our spark pools Additionally Azure s standard identity management capabilities can be fully applied here including managed identities service principles private endpoints and firewall rules Finally data security is alive and well with Azure giving us encryption at rest via transparent data encryption column and row security to restrict who can see what data and data masking for sensitive data SummaryAzure Synapse Analytics wants to be the cloud based backbone of your entire data infrastructure It offers the scalability and management features needed to meet the needs of the enterprise and has a usage based pricing model and serverless options to make Synapse Analytics viable for individuals and small businesses as well Overall Synapse Analytics is a very powerful and capable service for your data processing needs and integrates well into other offerings including Azure Machine Learning	2022-04-22 02:19:49
海外TECH	DEV Community	The 3 Core Machine Learning Tasks	https://dev.to/integerman/the-3-core-machine-learning-tasks-5cl	The Core Machine Learning Tasks The Three Core TasksLet s talk about the core machine learning tasks Classification Regression and Clustering These are the three tasks you ll want to focus on when learning data science This content is also available in video form on YouTubeNot only are these tasks very common things you ll want to do for your data science projects but these projects will help you build the skills and knowledge you need to perform the more specialized aspects of machine learning Let s get started ClassificationImagine you were a bank and had historical information about people who have taken out loans and whether or not those loans had been repaid Using this data set you could train a machine learning model to predict whether a person is likely to pay back a loan they re requesting This is an example of classification predicting what categorical label something might belong to given historic data In this case the label I m predicting would be whether the loan would be repaid and the features relevant for that might be things related to a person s annual income the value of the home the term of the loan and the amount being requested which might look like the following data BorrowedMonthsSalaryRepaid Yes No No Yes No Example Project Classifying Die HardAnother example of classification is a machine learning experiment I did last year around the movie Die Hard My wife and I were debating if Die Hard should be considered a Christmas movie To solve this problem I built a machine learning model around historical movie information that included both Christmas movies and non Christmas movies Once this model was trained I asked the model if Die Hard should be considered a Christmas movie and it was able to predict the expected value of the Is Christmas Movie label for that movie Both Die Hard and the loan approval models are examples of binary classification where something is going to be one of two possibilities Other examples might be predicting if a customer or employee will leave your organization or if a mole is cancerous Multi Class ClassificationSometimes you want to predict if something is one of several different possibilities When there are or more possibilities we call this multi class classification Example Project ESRB Game Rating PredictionFor example if you have an unreleased video game and wanted to predict the Entertainment Software Rating Board or ESRB rating for the game s content you could build a classification model and train it on historical games their content and the rating they were given This trained model would then be able to predict ESRB ratings for video games that had yet to be released and generate some degree of probability that a game might be in any given rating Using this I could determine how likely a new video game was to be given a specific rating given historical video game releases RegressionNext we have regression models If classification is all about predicting a single categorical label then regression is about predicting a single numerical label instead In other words we re no longer predicting what something is but instead we re predicting how much of something For example you could train a regression model to predict the how much a used car would sell for given historical data on recent used car sales in the area ModelYearMileageOriginal MSRPResell PriceRoad Hog Raging Puma Road Hog Teen Trainer Raging Puma Example Project Car Defrosting PredictionA regression experiment I did in the past involved predicting the number of minutes I d need to spend in the morning scraping off my car s windshield I built a data set over some time by automatically tracking overnight weather predictions and then manually recording the number of minutes I spent defrosting my car By the end of the winter I had a model that was trained sufficiently to be able to predict how much time I d need to scrape off my car s windshield Of course by the next winter we had a garage and my model was worthless but this was a good example of a regression model in action ClusteringFinally we reach clustering Clustering is the process of determining groups of data points based on their similarities Clustering is sometimes used for things like segmenting different types of users for marketing strategies based on their usage habits Clustering is also used for geographical data If I wanted to host events across the world to meet every person who watched this video in a given year a clustering algorithm could determine the optimal places to hold each one of those events Some of you would still need to travel farther than others but the average person s travel distance would be as good as we could make it ConclusionThat covers the basics of the three core types of machine learning classification regression and clustering As you get started with machine learning I strongly encourage you to start with classification or regression In fact a standard experiment for new data scientists is to start out with a binary classification experiment that predicts if a passenger on the Titanic would have lived or died based on their ticket information And no this is not a joke Check it out and see Until next time happy coding and keep learning	2022-04-22 02:13:12
海外TECH	DEV Community	What can you do in ML.NET with C#?	https://dev.to/integerman/what-can-you-do-in-mlnet-with-c-56l2	What can you do in ML NET with C What is ML NET ML NET is Microsoft s open source cross platform machine learning library for NET applications that allows you to perform machine learning tasks using C F or any other NET language Additionally ML NET supports models built in other machine learning frameworks such as TensorFlow ONNX Infer NET and others For those who do not yet have deep data science skills and knowledge of the various machine learning algorithms ML NET also offers AutoML which automates the training of certain types of machine learning models which helps you focus more on setting up the experiment and working with the trained model All of these things combine to make ML NET a very effective way to work with machine learning tasks using applications you already have and skills you already know In this article we ll use C to explore the high level tasks that ML NET can do and where to find more information about each type of machine learning task you might want to perform Note I also recommend reading using AutoML and ML NET to predict ESRB ratings for video games since it serves as an in depth intro to ML NET for a single task Installing ML NETML NET can be installed via NuGet Package Manager from Visual Studio for any project that supports NET Standard almost all NET projects can do this To add ML NET to a project go to NuGet Package Manager and install the latest version of Microsoft ML I also recommend you install Microsoft ML AutoML since many examples here feature that code and AutoML is a good way to get started with ML NET See Microsoft s documentation on NuGet Package Manager for more details on working with NuGet Package Manager Tasks Supporting AutoMLFirst I m going to highlight the five machine learning tasks in ML NET that are supported using AutoML These are easier tasks to get into due to their support for AutoML and so I will supply some code for each type of task Supplying full code and details for each one is something best done in a separate article so if you have interest in more detail into a specific task please ask and I ll be happy to provide additional content as I can Binary ClassificationBinary Classification tasks involve predicting a categorical label that something should be assigned to given a set of related features For example given some characteristics about a loan applicant a binary classification model would predict whether or not that loan should be approved or rejected Binary Classification tasks are limited to predicting a single column that has two possible values If there are more than two possible values that is a multi class classification task which we ll discuss next Code to run a binary classification experiment using AutoML might look something like the following public ITransformer PerformBinaryClassification IDataView trainingData IDataView validationData Set up the experiment MLContext context new MLContext uint maxSeconds BinaryClassificationExperiment experiment context Auto CreateBinaryClassificationExperiment maxSeconds Run the experiment and wait synchronously for it to complete ExperimentResult lt BinaryClassificationMetrics gt result experiment Execute trainingData validationData labelColumnName ShouldApproveLoan result BestRun ValidationMetrics has properties helpful for evaluating model performance double accuracy result BestRun ValidationMetrics Accuracy double fScore result BestRun ValidationMetrics FScore string confusionTable result BestRun ValidationMetrics ConfusionMatrix GetFormattedConfusionTable Return the best performing trained model ITransformer bestModel result BestRun Model return bestModel You could then use that trained model to make a prediction via the following code public LoanPrediction PredictBinaryClassification ITransformer bestModel IDataView trainingData LoanData loan MLContext context new MLContext Create an engine capable of evaluating one or more loans in the future PredictionEngine lt LoanData LoanPrediction gt engine context Model CreatePredictionEngine lt LoanData LoanPrediction gt bestModel trainingData Schema Actually make the prediction and return the findings LoanPrediction prediction engine Predict loan return prediction Here LoanData and LoanPrediction are classes representing a row in your dataset and the final prediction of your algorithm respectively Note ML NET also supports classification without using AutoML but for brevity that code is omitted from this article Multi Class ClassificationMulti Class classification tasks are very similar to binary classification tasks in that you are trying to predict a categorical value for a single labelled column given a set of features The key difference between a binary classification problem and a multi class classification problem is that with a binary classification problem there are only two possible values something might have while in a multi class classification problem there are three or more possible categories something might fall into Code for training a multi class classification experiment using AutoML might look like the following public ITransformer PerformMultiClassification IDataView trainingData IDataView validationData Set up the experiment MLContext context new MLContext uint maxSeconds MulticlassClassificationExperiment experiment context Auto CreateMulticlassClassificationExperiment maxSeconds Run the experiment and wait synchronously for it to complete ExperimentResult lt MulticlassClassificationMetrics gt result experiment Execute trainingData validationData labelColumnName RiskCategory result BestRun ValidationMetrics has properties helpful for evaluating model performance string confusionTable result BestRun ValidationMetrics ConfusionMatrix GetFormattedConfusionTable Return the best performing trained model ITransformer bestModel result BestRun Model return bestModel Beyond that the code for working with trained classification models is remarkably similar to the code for working with binary classification models Also like the binary classification models it is possible to work with multi class classification models without using AutoML For more code examples around multi class classification take a look at my article and video on using AutoML and ML NET to predict ESRB ratings for video games which also serves as an in depth introduction to ML NET for a single task RegressionRegression tasks involve predicting a numerical value given a set of features For example you could use a regression model to predict the price of gas given a known set of other factors or use regression to predict the length of time you might need to spend defrosting your car in the morning given overnight weather factors Any time you need to calculate a single numerical value you re likely dealing with a regression problem The code to perform model training for your regression experiment is similar to that of classification experiments public ITransformer PerformRegression IDataView trainingData IDataView validationData Set up the experiment MLContext context new MLContext uint maxSeconds RegressionExperiment experiment context Auto CreateRegressionExperiment maxSeconds Run the experiment and wait synchronously for it to complete ExperimentResult lt RegressionMetrics gt result experiment Execute trainingData validationData labelColumnName Temperature result BestRun ValidationMetrics has properties helpful for evaluating model performance double error result BestRun ValidationMetrics MeanAbsoluteError Return the best performing trained model ITransformer bestModel result BestRun Model return bestModel Note that the validation metrics for regression experiments are completely different than the validation metrics for classification experiments While classification experiments deal with the probability that something is given the correct category regression experiments deal with the distance between the predicted numerical value and the actual numerical value for known historical data Like both classification model types you don t need to use AutoML when training a regression model but it can be helpful if your knowledge of individual algorithms is limited RecommendationA recommendation algorithm is a variant of a regression algorithm With a recommendation algorithm you feed in data about different types of users and different ratings they ve given items in the past Given such a dataset a recommendation model can predict what rating a user would give to something they ve not yet interacted with before based on their similarity to the tastes of other known users Recommendation models are popular in movie music and product recommendation systems where repeat users are common and everyone benefits from users finding the content they ll like the most Recommendation is supported by AutoML and the code for recommendation is very similar to regression code public ITransformer PerformRecommendation IDataView trainingData IDataView validationData Set up the experiment MLContext context new MLContext uint maxSeconds RecommendationExperiment experiment context Auto CreateRecommendationExperiment maxSeconds Run the experiment and wait synchronously for it to complete ExperimentResult lt RegressionMetrics gt result experiment Execute trainingData validationData labelColumnName Rating result BestRun ValidationMetrics has properties helpful for evaluating model performance double error result BestRun ValidationMetrics MeanAbsoluteError Return the best performing trained model ITransformer bestModel result BestRun Model return bestModel Under the hood the recommendation algorithms use matrix factorization which is a more complicated topic See Microsoft s tutorial on matrix factorization for more details on recommendation systems without using AutoML There s also a fantastic article from Rubik s Code exploring the topic in additional depth RankingRanking is similar to a recommendation algorithm but is used to put items into a force order rank suitable for displaying search results Ranking systems are suitable for showing a list of ordered recommendations for a specific user or group of users The code is similar to the code we ve seen before though the validation metrics are significantly different public ITransformer PerformRanking IDataView trainingData IDataView validationData Set up the experiment MLContext context new MLContext uint maxSeconds RankingExperiment experiment context Auto CreateRankingExperiment maxSeconds Run the experiment and wait synchronously for it to complete ExperimentResult lt RankingMetrics gt result experiment Execute trainingData validationData labelColumnName Temperature result BestRun ValidationMetrics has properties helpful for evaluating model performance IEnumerable lt double gt gains result BestRun ValidationMetrics DiscountedCumulativeGains IEnumerable lt double gt normalizedGains result BestRun ValidationMetrics NormalizedDiscountedCumulativeGains Return the best performing trained model ITransformer bestModel result BestRun Model RankingEvaluatorOptions options new RankingEvaluatorOptions RankingMetrics metrics context Ranking Evaluate trainingData labelColumnName Label rowGroupColumnName Group scoreColumnName Score return bestModel Other Solution TypesNow let s take a high level look at each of the five machine learning tasks not currently supported through AutoML Code for each one of these is going to be more varied than this article can afford so reach out and ask for more content on the ones that interest you the most Forecasting Time Series DataForecasting involves predicting a batch of future regression values based on historical data When you are forecasting you are predicting values from some window into the future where each value predicted has a certain level of confidence level This works in a similar way to how a weather forecast might work Weather forecasts are most accurate with a lot of relevant historical data when predicting values in the near future They can be used to predict values some time in the future but the accuracy of those predictions goes down significantly as the time range goes on ClusteringClustering is used to group various data points together into groups based on similarities to nearby data points This can be used to determine what customers are similar to each other for marketing grouping for recommendations or other purposes When working with geographical data this can also be a great way of determining optimal locations for office placements or cell towers Clustering typically works by choosing an arbitrary number of clusters and allowing machine learning to follow a K Means clustering algorithm to optimize the central location of each cluster in order to minimize the overall distance from each data point to the center of its cluster Clustering algorithms also tend to try to space out clusters from each other when possible Anomaly DetectionAnomaly detection can be used to flag individual transactions as unusual for additional investigation Anomaly detection is often used for virus detection credit card fraud detection and identifying unusual network activity You can think of anomaly detection almost like an automated form of binary classification where something is either going to be normal or anomalous Image ClassificationImage classification is similar to binary or multi class classification but instead of working on numerical features it works on an image to determine what is featured in a given image Like classification problems you must provide ML NET with a wide variety of labelled images in different sizes lighting and arrangements featuring the things you are trying to detect in order for it to reliably classify images Object DetectionObject detection is like image classification but instead of telling you that an image is of a specific class object detection gives you an actual bounding box in the image telling you where that specific object is located Additionally object detection is capable of locating multiple objects in a single image which exceeds the limitations of image classification Object detection is a part of Azure Cognitive Services that is only available in ML NET via the Model Builder at the time of this writing ConclusionHopefully this article helps clarify some of the things that ML NET can achieve either with AutoML or without it Watch this blog and my YouTube channel for more content on ML NET in the future and let me know what you re most curious about learning In the meantime I recommend looking into Microsoft s documentation on ML NET for additional details or checking out their ML NET samples on GitHubThis post was featured as part of the C Advent event Be sure to check out other posts by fantastic members of the NET community	2022-04-22 02:06:44
Apple	AppleInsider - Frontpage News	Apple details mangrove conservation efforts made in India ahead of World Earth Day	https://appleinsider.com/articles/22/04/22/apple-details-mangrove-conservation-efforts-made-in-india-ahead-of-world-earth-day?utm_medium=rss	Apple details mangrove conservation efforts made in India ahead of World Earth DayApple has awarded the Applied Environmental Research Foundation AERF a grant to help protect India s coastal mangrove population in the fight against climate change Image Credit AppleA mere miles south of India s Mumbai lies Alibaug in India s Raigad district This small coastal town features a modest population of just over people unpaved roads palm trees and open air markets Read more	2022-04-22 02:07:43
金融	ニッセイ基礎研究所	中国経済の現状と当面の注目点－財政・金融・ゼロコロナの3つの政策運営に注目！	https://www.nli-research.co.jp/topics_detail1/id=70947?site=nli	しかし、中国政府含む中国人民銀行がコロナ対策のために財政金融をフル稼働させたため年月期には同増とプラス成長に転じ、その後も順調に持ち直して年月期には同増の高成長となった。	2022-04-22 11:51:53
金融	ニッセイ基礎研究所	消費者物価（全国22年3月）－コアCPIは22年4月以降、2％前後の伸びが続く見通し	https://www.nli-research.co.jp/topics_detail1/id=70948?site=nli	その後、エネルギー価格の上昇ペースは鈍化傾向が続くものの、円安による物価上昇圧力が高まる中で、食料品に加え、日用品や衣料品などでも価格転嫁の動きが広がることから、年中は前後の推移が続くことが予想される。	2022-04-22 11:51:16
金融	金融資本市場分析 | 大和総研グループ	企業のサステナビリティ情報の開示に関する国際的な基準案が公表	https://www.dir.co.jp/report/research/capital-mkt/esg/20220422_022988.html	後者は気候変動をテーマとした基準であり、投資家が企業価値に対する気候関連のリスクと機会の影響を評価できるようにする情報の開示を企業に求めるものである。	2022-04-22 11:15:00
ニュース	@日本経済新聞 電子版	2022年3月期の純利益は過去最高、今期も増益を見込む日本電産。それでも永守重信氏は「今の株価は耐えられない水準。1万円くらいで残っていれば私の出る幕はなかった」とCEO復帰に踏み切りました。 https://t.co/MAn6b2UVIv	https://twitter.com/nikkei/statuses/1517337432747876352		2022-04-22 03:00:09
ニュース	@日本経済新聞 電子版	「神業」味覚の紅茶鑑定士、神戸で厳選商品を販売 https://t.co/xI9Vc8UmZC	https://twitter.com/nikkei/statuses/1517336068521361408	鑑定士	2022-04-22 02:54:44
ニュース	@日本経済新聞 電子版	日立再編完了、支配から連帯へ　日立物流に少額資本残す https://t.co/17xmD5sqxk	https://twitter.com/nikkei/statuses/1517334289548865536	日立物流	2022-04-22 02:47:39
ニュース	@日本経済新聞 電子版	国会議員の文通費、改革道半ば　名称に「調査研究」入る https://t.co/UygT584u4N	https://twitter.com/nikkei/statuses/1517331278973919233	国会議員	2022-04-22 02:35:42
ニュース	@日本経済新聞 電子版	マスク氏Twitter買収案、言論インフラ防御策に再考の時 https://t.co/IMQQeGK7Sr	https://twitter.com/nikkei/statuses/1517329780915404800	twitter	2022-04-22 02:29:45
ニュース	@日本経済新聞 電子版	「強い円」は企業が創る　ルービン時代の米国に処方箋 https://t.co/PYrBLW7RNz	https://twitter.com/nikkei/statuses/1517327746065911808	米国	2022-04-22 02:21:39
ニュース	@日本経済新聞 電子版	マリウポリ近郊「集団墓地」200カ所以上　衛星画像解析 https://t.co/ICtYGIrzqA	https://twitter.com/nikkei/statuses/1517326253111787520	画像解析	2022-04-22 02:15:43
ニュース	@日本経済新聞 電子版	ゴーン被告に仏検察が国際逮捕状　米報道 https://t.co/WOCtQgf1wV	https://twitter.com/nikkei/statuses/1517324470465093632	逮捕状	2022-04-22 02:08:38
ニュース	@日本経済新聞 電子版	60代ライフの「幸せ」に必要な資産額とは https://t.co/B7RsO41f9W	https://twitter.com/nikkei/statuses/1517323226136809472	資産	2022-04-22 02:03:42
海外ニュース	Japan Times latest articles	Japan tells U.S. that recent yen falls are ‘sharp’	https://www.japantimes.co.jp/news/2022/04/22/business/suzuki-us-yen-moves/	policy	2022-04-22 11:04:05
ニュース	BBC News - Home	Boris Johnson and India's Narendra Modi to discuss defence and trade	https://www.bbc.co.uk/news/uk-politics-61183833?at_medium=RSS&at_campaign=KARANGA	india	2022-04-22 02:35:15
北海道	北海道新聞	日本橋で金属落下、通行人けが　東京、ビルからプレート	https://www.hokkaido-np.co.jp/article/672695/	東京都中央区日本橋室町	2022-04-22 11:18:00
北海道	北海道新聞	３月の消費者物価、０・８％上昇　７カ月連続、原油高で	https://www.hokkaido-np.co.jp/article/672686/	全国消費者物価指数	2022-04-22 11:13:00
北海道	北海道新聞	大麻で３等陸曹停職　陸自	https://www.hokkaido-np.co.jp/article/672685/	陸上自衛隊	2022-04-22 11:10:00
北海道	北海道新聞	当別町の道の駅付近でクマのような動物の目撃　運転手が通報	https://www.hokkaido-np.co.jp/article/672680/	当別町当別太	2022-04-22 11:08:45
北海道	北海道新聞	「ウマ娘」ファン日高地方に続々　モデルの名馬とふれあい　プレーヤー向け宿泊プラン好評	https://www.hokkaido-np.co.jp/article/672423/	新ひだか	2022-04-22 11:07:54
マーケティング	AdverTimes	LIFULL、3人の小説家・芸人が綴った「ソーシャルイシューストーリー」特設サイトをオープン	https://www.advertimes.com/20220422/article382454/	lifull	2022-04-22 02:23:46
海外TECH	reddit	[Post Game Thread] The Memphis Grizzlies (2-1) complete the 25-point comeback to defeat the Minnesota Timberwolves (1-2), 104-95, behind Desmond Bane's 26 points	https://www.reddit.com/r/nba/comments/u93k2p/post_game_thread_the_memphis_grizzlies_21/	Post Game Thread The Memphis Grizzlies complete the point comeback to defeat the Minnesota Timberwolves behind Desmond Bane x s points Box Scores NBA amp Yahoo nbsp GAME SUMMARY Location Target Center Clock Officials James Capers Josh Tiven and Scott Twardoski Team Q Q Q Q Total Memphis Grizzlies Minnesota Timberwolves nbsp TEAM STATS Team PTS FG FG P P FT FT OREB TREB AST PF STL TO BLK Memphis Grizzlies Minnesota Timberwolves nbsp PLAYER STATS Memphis Grizzlies MIN PTS FGM A PM A FTM A ORB DRB REB AST STL BLK TO PF Dillon BrooksSF Kyle AndersonPF Jaren Jackson Jr C Desmond BaneSG Ja MorantPG Xavier Tillman Ziaire Williams Tyus Jones Brandon Clarke De Anthony Melton Steven Adams Jarrett Culver John Konchar Minnesota Timberwolves MIN PTS FGM A PM A FTM A ORB DRB REB AST STL BLK TO PF Anthony EdwardsSF Jarred VanderbiltPF Karl Anthony TownsC Patrick BeverleySG D Angelo RussellPG Malik Beasley Jaden McDaniels Naz Reid Taurean Prince Greg Monroe Leandro Bolmaro Jake Layman Jordan McLaughlin Jaylen Nowell Josh Okogie rnbapgtgenerator by u fukr submitted by u widesheep to r nba link comments	2022-04-22 02:04:23
海外TECH	reddit	GRIZZLIES WIN GAME 3 104-95 POST GAME [4/21/22]	https://www.reddit.com/r/memphisgrizzlies/comments/u93l67/grizzlies_win_game_3_10495_post_game_42122/	GRIZZLIES WIN GAME POST GAME Box Scores NBA amp Yahoo nbsp GAME SUMMARY Location Target Center Clock Officials James Capers Josh Tiven and Scott Twardoski Team Q Q Q Q Total Memphis Grizzlies Minnesota Timberwolves nbsp TEAM STATS Team PTS FG FG P P FT FT OREB TREB AST PF STL TO BLK Memphis Grizzlies Minnesota Timberwolves nbsp PLAYER STATS Memphis Grizzlies MIN PTS FGM A PM A FTM A ORB DRB REB AST STL BLK TO PF Dillon BrooksSF Kyle AndersonPF Jaren Jackson Jr C Desmond BaneSG Ja MorantPG Xavier Tillman Ziaire Williams Tyus Jones Brandon Clarke De Anthony Melton Steven Adams Jarrett Culver John Konchar Minnesota Timberwolves MIN PTS FGM A PM A FTM A ORB DRB REB AST STL BLK TO PF Anthony EdwardsSF Jarred VanderbiltPF Karl Anthony TownsC Patrick BeverleySG D Angelo RussellPG Malik Beasley Jaden McDaniels Naz Reid Taurean Prince Greg Monroe Leandro Bolmaro Jake Layman Jordan McLaughlin Jaylen Nowell Josh Okogie rnbapgtgenerator by u fukr submitted by u sms to r memphisgrizzlies link comments	2022-04-22 02:05:54