投稿時間:2022-08-09 02:16:02 RSSフィード2022-08-09 02:00 分まとめ(19件)

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AWS	AWS Machine Learning Blog	MLOps at the edge with Amazon SageMaker Edge Manager and AWS IoT Greengrass	https://aws.amazon.com/blogs/machine-learning/mlops-at-the-edge-with-amazon-sagemaker-edge-manager-and-aws-iot-greengrass/	MLOps at the edge with Amazon SageMaker Edge Manager and AWS IoT GreengrassInternet of Things IoT has enabled customers in multiple industries such as manufacturing automotive and energy to monitor and control real world environments By deploying a variety of edge IoT devices such as cameras thermostats and sensors you can collect data send it to the cloud and build machine learning ML models to predict anomalies failures …	2022-08-08 16:48:21
海外TECH	DEV Community	Let’s get data-rich with Next.js visualizations ✨	https://dev.to/kulkarniankita9/lets-get-data-rich-with-nextjs-visualizations-2no0	Let s get data rich with Next js visualizations Next js is a popular JS framework and is incredibly powerful It is one of my favourite frameworks as it does a great job of making its APIs simple and easy to understand It has become the production framework for React and rightly so We often tend to visualize things and concepts to understand them better If you want to master Next js it provides many techniques to fetch data in a manner that is right for your use case You need to know how and when to fetch it that s all There are many powerful ways to fetch data Here are a few of them Now that we know what they are called let s review how you can fetch data in Next js in the form of visuals Static site Generation SSG SSG is great for static sites If you have a lot of static content on your site SSG is the right fit Without API calls i e No external data ‍ ️It is pre rendered by default since the data is available to us statically We are not invoking any APIs here so Next js will store it at build time For example the About page static navigation data headers etc With API calls i e external data SSG pre generates i e downloads all the static content at build time before you deploy your site and then serves it directly which means it won t make live API calls for every user but instead fetch from the build The same HTML will be used for every request making it lightning fast️ Whenever you can use SSG as it will help a lot with performance Incremental Static Regeneration ISR ️This method is one of my favourite way of fetching data It gives you the best of both worlds ー SSG SSR You can opt in to re generate a specific page or pages at a specific interval Let s say you set the interval to be seconds and the first user that visits gets stale data but the user after will get fresh data ‍ ️For example let s say you run a coffee ️store and one of your products gets popular Now you want to get the latest data about that store but that data won t get updated every minute With ISR you can say I want that store with store id to get re generated every minutes This way you still reap the benefits of pre generating the data at build time but also take advantage of fresh data Example Old Twitter tweets not many users will visit old tweets so it is okay for old tweets to have stale data although with ISR you can get fresh data for the following request Server side Rendering SSR 🪴In SSR HTML is generated for every request on the server You want to use SSR for a news feed where we always want the latest most relevant news but also want to pre render data as that is good for SEO SSR only runs on the server and not on the browser Client side Rendering CSR ‍This is the traditional way of rendering content to the client With CSR we don t need to pre render data and instead fetch data at runtime in a useEffect The browser is responsible for rendering content to the client This can impact performance as the data is not cached I hope you found this post useful If you did comment on how you are planning to use Next js or are using it already If you would like to learn more about Next js I have a course on platforms Udemy andZero to Mastery	2022-08-08 16:34:01
海外TECH	DEV Community	Regression in machine learning	https://dev.to/joy_chiorlu/regression-in-machine-learning-3pcp	Regression in machine learningOne of the most popular uses of machine learning models particularly in supervised machine learning is to solve regression problems The relationship between an outcome or dependent variable and independent variables is something that algorithms are trained to grasp In laymans terms it means fitting a function from a specified family of functions to the sampled data under some error function Prediction forecasting time series modeling and establishing the causal connection between variables are its key uses This fitting of function serves two purposes Estimating missing data within your data rangeEstimating future data outside your data rangeAlthough the common application is predicting future data outside your data range after it has been trained The Machine Learning regression algorithm is similar to our linear algebra s line of best fit Let s go back in memory lane to our elementary mathematics In maths we were given X and y points and asked to plot a linear graph then in exercises we were asked to find the value of y for which x is from our data I believe we all went ahead in plotting our graph and looking for the corresponding value of y This is very similar to our regression problem but now we want the machine to do it for us X here are the variables we want to use to predict y while y is what we want to find out Let s have this basic understanding before we move forward Terminologies used in RegressionDependent Variable Y  The dependant variable is the main factor in Regression analysis that we wish to predict or understand It is also known as the target value or labelIndependent Variable X  The independent variables are the elements that influence the dependent variables or are used to predict the values of the dependent variables It is usually referred to as our features Outliers  Outlier is an observation that contains either a very low value or a very high value in comparison to other observed values An outlier should be avoided as it might hurt the outcome Multicollinearity  If the independent variables are more highly correlated with each other than other variables then such a condition is called Multicollinearity It should not be present in the dataset because it creates problems while ranking the most affecting variable Underfitting and Overfitting  If our algorithm works well with the training dataset but not well with the test dataset then such a problem is called Overfitting And if our algorithm does not perform well even with the training dataset then such a problem is called underfitting Uses of regression models Common uses for machine learning regression models include Forecasting continuous outcomes like house prices stock prices or sales Predicting the success of future retail sales or marketing campaigns to ensure resources are used effectively Predicting customer or user trends such as on streaming services or e commerce websites Analyzing datasets to establish the relationships between variables and output Predicting interest rates or stock values based on a multitude of factors Creating time series visualizations Types of regression analysisLet s now discuss the various methods through which we can perform regression Regression can be carried out using a variety of different methods in machine learning Machine learning regression is accomplished using a variety of well known techniques The various methods could use various numbers of independent variables or handle various kinds of data A different relationship between the independent and dependent variables may also be assumed by various kinds of machine learning regression models Linear regression techniques for example presume that the relationship is linear and would be ineffective with nonlinear datasets Types of regression modelsSimple Linear RegressionMultiple linear regressionLogistic regressionSupport Vector RegressionDecision Tree RegressionRandom Forest Regression Simple linear regressionSimple Linear Regression is an approach that plots a straight line within data points to minimize the error between the line and the data points In this scenario the connection between the independent and dependent variables is considered to be linear This method is straightforward because it is used to investigate the relationship between the dependent variable and one independent variable Outliers may be common in simple linear regression due to the straight line of best fit A statistical regression technique used for predictive analysis is called linear regressionIt is one of the most basic and straightforward algorithms that use regression to illustrate the relationship between continuous variables In machine learning it is used to solve the regression problem The term linear regression refers to a statistical method that displays a linear relationship between the independent variable X axis and the dependent variable Y axis Such linear regression is known as simple linear regression if there is only one input variable x Additionally this type of linear regression is known as multiple linear regression if there are many input variables Y aX bwhere Y what we are trying to predictX features or variables we would be used to predict the value of Ya slope of the lineb Intercept at the Y axis Similar to our maths linear algebra right Multiple linear regressionWhen more than one independent variable is used multiple linear regression is used Polynomial regression is an example of a multivariate linear regression technique It is a sort of multiple linear regression used if there is more than one independent variable When numerous independent variables are included it achieves a better fit than simple linear regression When plotted in two dimensions the outcome would be a curved line fitting to the data points Logistic regression is employed when the dependent variable can have one of two values such as true or false or success or failure Logistic regression models can be used to forecast the likelihood of occurrence of a dependent variable The output values must typically be binary A sigmoid curve can be used to depict the relationship between the dependent and independent variables Polynomial Regression Polynomial Regression is a sort of regression that uses a linear model to represent the non linear dataset While it fits a non linear curve between the value of x and related conditional values of y it is comparable to multiple linear regression Assume there is a dataset with sample data that are distributed in a non linear form in this situation linear regression will not best match those data points Polynomial regression is required to cover such data points In Polynomial regression the original characteristics are transformed into polynomial features of a specific degree and then modeled using a linear model Note This differs from Numerous Linear regressions in that in Polynomial regression a single element has different degrees rather than multiple variables with the same degree Support Vector Regression Support Vector Machine is a supervised learning algorithm that can be used for regression as well as classification problems So if we use it for regression problems then it is termed Support Vector Regression Support Vector Regression is a regression algorithm that works for continuous variables Below are some keywords which are used in Support Vector Regression Kernel  It is a function that converts lower dimensional data to higher dimensional data Hyperplane  In general SVM is a line that divides two classes while SVR is a line that helps forecast continuous variables and covers the majority of the data points Boundary line  These are two lines that are set aside from the hyperplane and creates a margin for data points are known as boundary lines Support vectors  The data points closest to the hyperplane and opposing class is known as support vectors In SVR we always try to determine a hyperplane with a maximum margin so that the maximum number of data points are covered in that margin The basic purpose of SVR is to consider as many data points as possible within the boundary lines and the hyperplane best fit line must contain as many data points as possible Decision Tree RegressionDecision Trees are a supervised learning method for solving classification and regression issues It is capable of resolving issues with category and numerical data Decision Tree regression constructs a tree like structure in which each internal node represents a test for an attribute each branch indicates the test s result and each leaf node provides the ultimate decision or result Starting with the root node parent node dataset a decision tree is built which divides into left and right child nodes subsets of the dataset These child nodes are further divided into their children nodes and themselves become the parent node of those nodes Consider the below image Random forest is a powerful supervised learning algorithm capable of handling both regression and classification problems The Random Forest regression is an ensemble learning method that combines multiple decision trees and predicts the final output based on the average of each tree output The combined decision trees are called base models and they can be represented more formally as g x f x f x f x Random forest uses the Bagging or Bootstrap Aggregation technique of ensemble learning in which aggregated decision tree runs in parallel and do not interact with each other With the help of Random Forest regression we can prevent Overfitting in the model by creating random subsets of the dataset Random ForestA random forest is a meta estimator that fits several categorizing decision trees on different sub samples of the dataset and utilizes averaging to increase predicted accuracy and control over fitting Some of the important parameters are highlighted below n estimators ーthe number of decision trees you will be running in the modelcriterion ーThis variable lets you choose the criterion loss function that will be used to decide model outcomes We can choose between loss functions like mean squared error MSE and mean absolute error MAE MSE is the default max depth ーthis sets the maximum possible depth of each treemax features ーthe maximum number of features the model will consider when determining a splitbootstrap ーthe default value for this is True meaning the model follows bootstrapping principles defined earlier max samples ーThis parameter is only effective if bootstrapping is set to True otherwise it has no effect When True this variable specifies the largest size of each sample for each treeOther important parameters are min samples split min samples leaf n jobs and others that can be read in the sklearn s RandomForestRegressor documentation here We would be focusing on the linear regression model in this article Subsequent articles showing illustrations for each of the following models would be released at a later dateA practical illustration of Linear Regression is shown in the code below The code depicts a multiple linear regression However the same code can be run for a simple linear regression model As a bonus we first performed a quick analysis of the data before training importing our librariesimport numpy as npimport pandas as pdimport matplotlib pyplot as plt matplotlib inline importing our datasetdataset pd read csv content Real estate csv brief overview of what our data looks likedataset head Getting descriptive information from our datadataset describe dataset info Carrying out our data analysis to see correlations between our dataimport seaborn as snssns jointplot dataset X transaction date dataset Y house price of unit area sns jointplot dataset X house age dataset Y house price of unit area sns jointplot dataset X distance to the nearest MRT station dataset Y house price of unit area sns jointplot dataset X number of convenience stores dataset Y house price of unit area sns jointplot dataset X latitude dataset Y house price of unit area sns jointplot dataset X longitude dataset Y house price of unit area sns pairplot dataset sns lmplot x X latitude y Y house price of unit area data dataset sns lmplot x X longitude y Y house price of unit area data dataset Splitting our data into a training set and testing sety dataset Y house price of unit area X dataset X transaction date X house age X distance to the nearest MRT station X number of convenience stores X latitude X longitude from sklearn model selection import train test splitX train X test y train y test train test split X y test size random state Training the simple linear model on the training setfrom sklearn linear model import LinearRegressionregressor LinearRegression regressor fit X train y train Predicting the test resultsy pred regressor predict X test Visualising the test set resultsplt scatter y test y pred plt xlabel Years of Experience plt ylabel Salary Calculating the mean absolute error mean sqaured error and the root mean squared errorfrom sklearn import metrics Evailuating our modelprint MAE metrics mean absolute error y test y pred print MSE metrics mean squared error y test y pred print RMSE np sqrt metrics mean squared error y test y pred We move ahead to exploring the residuals to ensure everything is alright with our codesns distplot y test y pred bins You can check out the full code here ConclusionIn summary regression models just help us predict whether we can buy a house based on some predetermined independent variables The machine learns the pattern and can predict past and future occurrences	2022-08-08 16:12:22
Apple	AppleInsider - Frontpage News	Ex-Apple inventor describes how his VR patent works to solve car motion sickness	https://appleinsider.com/articles/22/08/08/ex-apple-inventor-describes-how-his-vr-patent-works-to-solve-car-motion-sickness?utm_medium=rss	Ex Apple inventor describes how his VR patent works to solve car motion sicknessYouTube personality Mark Rober has talked about his time at Apple including the reasoning behind his work on an augmented virtual display patent for the long rumored Apple Car Mark Rober is best known as a YouTube star teaching science with fantastical experiments and explaining the principles at play He is less known as a former employee at Apple who came up with an idea that could end up being used in the Apple Car In a clip from the Waveform podcast Rober tells MKBHD about how he worked at Apple at the same time as he was gaining fame on Google s video platform Read more	2022-08-08 16:59:56
Apple	AppleInsider - Frontpage News	With iRobot acquisition, Amazon wants to scan every inch of your home	https://appleinsider.com/articles/22/08/08/with-irobot-acquisition-amazon-wants-to-scan-every-inch-of-your-home?utm_medium=rss	With iRobot acquisition Amazon wants to scan every inch of your homeAmazon wants to be everywhere inside your home and for those concerned about their data and privacy that s a dangerous prospect iRobot vacuumOn Friday the retail juggernaut announced a billion acquisition of iRobot the manufacturer of the Roomba series of robotic vacuum cleaners Although not yet approved many privacy experts are already concerned about the deal Read more	2022-08-08 16:35:41
海外TECH	Engadget	Less than 1 percent of Netflix’s subscribers are playing its games	https://www.engadget.com/netflix-less-than-1-percent-play-games-164305915.html?src=rss	Less than percent of Netflix s subscribers are playing its gamesNetflix s entry into the gaming market is off to a slow start According to an analysis performed by Apptopia on behalf of CNBC the streaming giant s games have been downloaded a total of million times and average about million daily users Put another way less than one percent of Netflix s million customers are taking advantage of the games included in their subscriptions Netflix did not immediately respond to Engadget s request for comment In the past the company indicated it did not expect its gaming division to be profitable immediately “We re going to be experimental and try a bunch of things Netflix COO Greg Peters told investors during the company s fourth quarter earnings calls last year Still the question that s probably on everyone s mind is how long Netflix is willing to wait to see if it made the right bet especially after it lost nearly one million subscribers during its most recent quarter Other lofty bets ーlike the company s in house fan blog Tudum ーwere the subject of cutbacks after only a few months of spending The company has shared precious few details on how much it has spent expanding its portfolio beyond TV shows and movies but most signs point to a significant investment Earlier this year the company paid million to acquire Next Games the studio behind Stranger Things Puzzle Tales More recently it secured exclusive mobile rights to beloved indie titles like Spiritfarer and Into The Breach The company is unlikely to make similar investments in the future if its current ones don t pan out	2022-08-08 16:43:05
海外TECH	Engadget	Lucid Air will soon have a 'Stealth Look' trim option	https://www.engadget.com/lucid-air-stealth-look-trim-option-160031181.html?src=rss	Lucid Air will soon have a x Stealth Look x trim optionLucid is embracing the dark side with a new trim option for its Air electric vehicle The idea behind Stealth Look is to give the car a darker and overtly sporting personality according to the automaker If you select this option Lucid will swap out exterior components that have a platinum finish for versions with a darker appearance with black gloss and satin graphite accents on some parts Among the elements that will have this look are the mirror caps the frame for the glass roof the trim around the headlights and taillights and the front nose blade The wheel designs and finishes will get a Stealth Look makeover as well with a different look for each size LucidStealth Look is available for the Air Grand Touring Performance Air Grand Touring and Air Touring variants It can be applied to any of the exterior colors Stellar White Infinite Black Cosmos Silver Quantum Grey and Zenith Red Lucid plans to introduce the option early next year though you can configure an Air with Stealth Look starting this Wednesday Whether you ll actually be able to get your hands on an Air at all anytime soon is another matter Earlier this month Lucid once again revised down its production target for this time from to between and units Between the start of the year and the beginning of August it built just vehicles attributing the blame to parts and materials shortages In May the company recalled every Air it manufactured until that point in over wiring concerns	2022-08-08 16:00:31
海外科学	BBC News - Science & Environment	Causes of deadly dry-lightning wildfires revealed	https://www.bbc.co.uk/news/science-environment-62406676?at_medium=RSS&at_campaign=KARANGA	california	2022-08-08 16:24:19
金融	金融庁ホームページ	アクセスＦＳＡ第228号について公表しました。	https://www.fsa.go.jp/access/index.html	アクセス	2022-08-08 17:18:00
金融	金融庁ホームページ	BIS決済・市場インフラ委員会および証券監督者国際機構による市中協議文書「清算機関のノンデフォルト・ロス対応実務に関するディスカッション・ペーパー」の公表について掲載しました。	https://www.fsa.go.jp/inter/ios/20220808.html	清算機関	2022-08-08 17:00:00
金融	金融庁ホームページ	金融庁職員の新型コロナウイルス感染について公表しました。	https://www.fsa.go.jp/news/r4/sonota/20220808.html	新型コロナウイルス	2022-08-08 17:00:00
金融	金融庁ホームページ	金融庁の業務委託先の従業員の新型コロナウイルス感染について公表しました。	https://www.fsa.go.jp/news/r4/sonota/20220808_2.html	新型コロナウイルス	2022-08-08 17:00:00
ニュース	BBC News - Home	Downing Street rules out immediate cost-of-living measures	https://www.bbc.co.uk/news/uk-politics-62467987?at_medium=RSS&at_campaign=KARANGA	government	2022-08-08 16:36:31
ニュース	BBC News - Home	Thornton Heath: Girl, 4, killed as explosion destroys house	https://www.bbc.co.uk/news/uk-england-london-62462955?at_medium=RSS&at_campaign=KARANGA	dizzy	2022-08-08 16:20:16
ニュース	BBC News - Home	UK heatwave: UK set for new heatwave as temperatures head to 35C	https://www.bbc.co.uk/news/uk-62468842?at_medium=RSS&at_campaign=KARANGA	heatwave	2022-08-08 16:50:11
ニュース	BBC News - Home	Ryan Giggs: Ex-Man Utd and Wales star headbutted ex-girlfriend - court	https://www.bbc.co.uk/news/uk-wales-62423393?at_medium=RSS&at_campaign=KARANGA	behaviour	2022-08-08 16:16:00
ニュース	BBC News - Home	Commonwealth Games: England secure record haul of 176 medals at Birmingham 2022	https://www.bbc.co.uk/sport/commonwealth-games/62468272?at_medium=RSS&at_campaign=KARANGA	commonwealth	2022-08-08 16:30:01
北海道	北海道新聞	大気不安定、道内各地で大雨　北斗市、６千人超に避難指示　道南は９日も激しい雨に警戒	https://www.hokkaido-np.co.jp/article/715706/	激しい雨	2022-08-09 01:29:21
北海道	北海道新聞	宇宙や鉱山の現場、ＶＲで体験　北大に３６０度シアター	https://www.hokkaido-np.co.jp/article/715733/	鉱山	2022-08-09 01:12:07