投稿時間:2022-01-05 21:33:47 RSSフィード2022-01-05 21:00 分まとめ(49件)

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IT	気になる、記になる…	Anker、最大100Wの新型USB充電器｢Anker Nano II 100W (Anker 736)｣を発表	https://taisy0.com/2022/01/05/150428.html	ankernanoiiwanker	2022-01-05 11:44:29
python	Pythonタグが付けられた新着投稿 - Qiita	Pythonのキューは3種類ある	https://qiita.com/84zume/items/f448070dd95e904ce61c	listcollectionsdequequeueQueuelistlistで実現する場合は、appendとpopを利用します。	2022-01-05 20:35:40
python	Pythonタグが付けられた新着投稿 - Qiita	Streamlit Sharingでシークレット情報を扱う	https://qiita.com/yuu999/items/e56fe82e61db0f74f9cb	Secretsの設定StreamlitSharingにてホストしたサイトの設定からSettingsを選択し、Secretsの設定を行います。	2022-01-05 20:27:00
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	pythonでnilガード的な書き方をして良いでしょうか？	https://teratail.com/questions/376778?rss=all	pythonでnilガード的な書き方をして良いでしょうか表題の通りなのですが、pythonでrubyで言うところのnilガード的な書き方はしても良いと思われますかpylintでは怒られないような印象ですが・・・次のような書き方です。	2022-01-05 20:55:23
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	PythonのOpencvで.avi動画を読み込みたい	https://teratail.com/questions/376777?rss=all	PythonのOpencvでavi動画を読み込みたい前提・実現したいことJupyternbspNotebookMacで『データ分析本ノック』第章を読みながら画像認識を行っています。	2022-01-05 20:50:53
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	python 関数の戻り値の処理について	https://teratail.com/questions/376776?rss=all	python関数の戻り値の処理についてPythonの質問です。	2022-01-05 20:50:25
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	loginシステムをつくっていたら、#2	https://teratail.com/questions/376775?rss=all	loginシステムをつくっていたら、前提・実現したいことloginシステムを作っています発生している問題・エラーメッセージこのページは動作していませんreminukuhennsilyuusilyagaでは現在このリクエストを処理できません。	2022-01-05 20:43:56
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	pip installでUnicodeDecodeError: 'cp932' codec can't decode が出る	https://teratail.com/questions/376774?rss=all	pipinstallでUnicodeDecodeErrorxcpxcodeccanxtdecodeが出る前提・実現したいことGithubこちらのレポジトリからpipnbspinstallがしたいのですが、エラーが出てしまいます。	2022-01-05 20:43:46
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	関数中での関数の呼び出し	https://teratail.com/questions/376773?rss=all	関数中での関数の呼び出し関数の中で関数を呼び出すことでつまづいています。	2022-01-05 20:41:40
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	DjangoでPage not found (404)エラー	https://teratail.com/questions/376772?rss=all	DjangoでPagenotfoundエラーDjangoでプロジェクトを作成してサーバーを起動し外部のパソコンからブラウザで接続を試みたところPagenbspnotnbspfoundnbspエラーが発生してみることができませんでした。	2022-01-05 20:41:08
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	【Power Apps】複数条件でフィルター処理	https://teratail.com/questions/376771?rss=all	【PowerApps】複数条件でフィルター処理PowernbspAppsのデータテーブルを複数条件でフィルター処理したいです。	2022-01-05 20:39:07
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	unity ml-agents トレーニング時のエラーを解消したい	https://teratail.com/questions/376770?rss=all	・その後、unity上で実行ボタンを押したが、agentが動かない動画上だと自動で動いて学習している。	2022-01-05 20:38:38
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	左右の余白を消したい	https://teratail.com/questions/376769?rss=all	左右の余白を消したいこのように、端に余白があります。	2022-01-05 20:27:56
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	HTML textとtextareaのフォントを合わせたい	https://teratail.com/questions/376768?rss=all		2022-01-05 20:24:45
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	スプレッドシートのIMPORTXML関数入力時、Xpathが認識されない	https://teratail.com/questions/376767?rss=all	スプレッドシートのIMPORTXML関数入力時、Xpathが認識されない前提・実現したいことIMPORTXML関数を使ってWEBページ上の値をスプレッドシートに入力したいです。	2022-01-05 20:17:16
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	【JQuery】pagepiling.jsで１ページスクロールにしwavify.jsで背景に波を付けたい	https://teratail.com/questions/376766?rss=all	【JQuery】pagepilingjsでページスクロールにしwavifyjsで背景に波を付けたいpagepilingjsでページスクロールにしwavifyjsで背景に波を付けたい現在DreamWeaverを使用しWEBデザインのポートフォリオを作ってます。	2022-01-05 20:02:50
Ruby	Rubyタグが付けられた新着投稿 - Qiita	【Rails】redirect_backとは	https://qiita.com/mmaumtjgj/items/b8992a9926134801cfaf	irectbackfallbacklocation	2022-01-05 20:55:01
Ruby	Rubyタグが付けられた新着投稿 - Qiita	devise_token_auth使用時のRspecの認証処理のやり方	https://qiita.com/soicchi/items/a8003e1510a0b17a07de	方法ここでは、devisetokenauthを使用してトークン認証をしている場合と、deviseを使用してトークンを使わない認証の場合に分けて記載します。	2022-01-05 20:24:24
Docker	dockerタグが付けられた新着投稿 - Qiita	コンテナにgitの指定バージョンをインストールする	https://qiita.com/error484/items/c137d6c2c570e92c68eb	コンテナにgitの指定バージョンをインストールする指定バージョンをインストールする為にmakeを行います。	2022-01-05 20:51:44
Docker	dockerタグが付けられた新着投稿 - Qiita	コンテナにNode.jsの指定バージョンをインストールする	https://qiita.com/error484/items/3655d6928385f80c75b9	コンテナにNodejsの指定バージョンをインストールするnvmを用いてインストールを行います。	2022-01-05 20:46:05
Docker	dockerタグが付けられた新着投稿 - Qiita	（3時間超・14本の動画）NestJS, Puppeteer, Docker, Supabase, YouTube Data APIが学べる開発講座を作りました	https://qiita.com/tsuyopon_xyz/items/c15fb6dae96e491ab28a	時間超・本の動画NestJSPuppeteerDockerSupabaseYouTubeDataAPIが学べる開発講座を作りました解説動画で学習する技術ツールYouTubeの再生リスト本の動画で以下の技術を取り上げて解説しました。	2022-01-05 20:08:02
GCP	gcpタグが付けられた新着投稿 - Qiita	GCP/DDNS/Let's Encrypt/できるだけ費用をかけずにインターネット上にblogサイトを立ち上げる	https://qiita.com/goroshigeno/items/def4103d34f4573c9aa4	感想クラウドIaaSの、早い、安いは恐るべし一からなんでも準備する時代ではないのだなと思う反面、仕組みがわかってないとトラブルシュートできないんじゃないかと。	2022-01-05 20:39:24
Git	Gitタグが付けられた新着投稿 - Qiita	コンテナにgitの指定バージョンをインストールする	https://qiita.com/error484/items/c137d6c2c570e92c68eb	コンテナにgitの指定バージョンをインストールする指定バージョンをインストールする為にmakeを行います。	2022-01-05 20:51:44
Ruby	Railsタグが付けられた新着投稿 - Qiita	【Rails】redirect_backとは	https://qiita.com/mmaumtjgj/items/b8992a9926134801cfaf	irectbackfallbacklocation	2022-01-05 20:55:01
Ruby	Railsタグが付けられた新着投稿 - Qiita	devise_token_auth使用時のRspecの認証処理のやり方	https://qiita.com/soicchi/items/a8003e1510a0b17a07de	方法ここでは、devisetokenauthを使用してトークン認証をしている場合と、deviseを使用してトークンを使わない認証の場合に分けて記載します。	2022-01-05 20:24:24
海外TECH	MakeUseOf	Robinhood Launches Cryptocurrency Wallets: How to Sign Up to the Beta	https://www.makeuseof.com/robinhood-launches-crypto-wallets-how-to-sign-up/	official	2022-01-05 11:45:11
海外TECH	MakeUseOf	Sony Announces PlayStation VR2: Everything We Know So Far	https://www.makeuseof.com/sony-playstation-vr2-specs-details/	mountain	2022-01-05 11:35:12
海外TECH	MakeUseOf	Do Your Philips Hue Bulbs Keep Turning Back On? How to Fix Them	https://www.makeuseof.com/philips-hue-bulbs-keep-turning-on-fix/	lights	2022-01-05 11:15:12
海外TECH	MakeUseOf	The 10 Best Gaming Smartphones	https://www.makeuseof.com/tag/best-gaming-smartphone/	gamer	2022-01-05 11:09:10
海外TECH	DEV Community	How to implement Factory Design Pattern using Java?	https://dev.to/rohan2596/how-to-implement-factory-design-pattern-using-java-cn1	How to implement Factory Design Pattern using Java Design PatternHelloIts Rohan KadamMaybe you are a newbie to coding‍or programming maybe experiencedor maybe a FrontEnd Developer or BackEnd Developer you all have may have come across the word Design Pattern as a principle or as a concept The design pattern is a way or approach to writing code or developing an application In this article we try to answer certain questions What is Design Pattern Why implement a design pattern How to implement a Design pattern Factory What is Design Pattern Design patterns are the solutions to commonly occurring problems in software design They are like pre fabricated blueprints that we can customize to solve a recurring design problem in your code They are not pieces of code or libraries that can be imported and used in the development of software or solving a particular problem We can follow the pattern details and implement a solution that suits the realities of your own program We often confused ourself between design pattern and algorithms While an algorithm always defines a clear set of actions that can achieve some goal a pattern is a more upper level description of a solution An analogy to an algorithm is a cooking dish both have clear steps to achieve a goal They re not libraries or modules they re guidelines you integrate into the core of your designs giving you a leg up in creating flexible and maintainable object oriented systems Why implement a Design Pattern We have encountered design patterns only in a nutshell they re general object oriented solutions that you can use in your own designs Crafted by experienced object oriented practitioners design patterns can make your designs more flexible more resilient to change and easier to maintain So if design patterns still aren t part of your development toolbelt here are five reasons you should add them →Make our life easier by not reinventing the wheel →Improve our object oriented Skills →Recognize Patterns in libraries and languages →Use the power of a shared vocabulary →Find Truth and beauty How to implement a Factory Design Pattern Before jumping directly into the implementation of the design pattern we need to answer certain questions such as What is a Factory design pattern Why incorporate the design pattern and finally How to implement a Factory Design Pattern →What is Factory Design Pattern The factory design pattern is part of the Creational Design Pattern Family it let us create an interface over subclass and let subclass do business logic work A real life example will be Imagine you have run a big delivery business you got to deliver the package to a consumer now the question arises what should use you used delivery the particular car truck or bicycle So to solution could be we give input as size and weight of a package based on that we decide which would be the best possible mode of transportation That s how the Factory pattern works in real life Definition The Factory pattern is a design pattern that defines an interface or abstract class for creating an object but let the subclasses decide which class to instantiate →Why implement Factory Design Pattern Factory allows us to follow the Design Principle SOLID It fosters loose coupling by eliminating the need to bind application specific classes into the code It allows us to introduce new code without affecting the existing code structure like Plug and Play Model →How to Implement Factory Design Pattern In section we try to implement Factory Design Pattern using Java For example we use the delivery manager example for selecting the mode of transportation Step →What input should the delivery manager give As a Delivery Manager he she should give info about the package like size and weight in our example we are using PackageInfo to the job Step →What will be various modes for making a delivery The various modes of delivering packages are car bicycleand truck We have created three class defining the modes for delivery According to the definition of Factory Pattern we need to define an interface or abstract class in our case we have created an interface named PackageDelivery We have created subclasses that are implemented by the PackageDelivery interface →Delivery by bicycle →Delivery by Car →Delivery by TruckStep →What will be my Factory Class or Manager Class In Example a Delivery Manager will be a factory Class or manager class that is responsible for choosing the delivery method based on package size According to the definition we are letting the subclasses decide which class to instantiate Step →How to test the Factory Design Pattern To test our Factory Design Pattern we are giving different packages with different different Packages sizes like SMALL LARGE and MEDIUMStep →What will be the Output for Factory Pattern Below console output helps to understand how the Manager Factory Class is selecting the mode of transportation based on Package Info Where to find the Codebase for Factory Design Pattern On Github GitHub Rohan Design pattern examples at pattern factory Conclusion In the article we tried to answer some questions related to Design Pattern and especially Factory Design Pattern how it helps developers around the world to write better code and build faster applications Factory Design Pattern allows us to implement the SOLID principle effectively may it is loose coupling or Single responsibility Please do share and likeif you find the article useful Follow me on medium Rohan Ravindra Kadam and on Twitter at rohankadam Bibliography reasons to finally learn design patternsFactory Method	2022-01-05 11:52:17
海外TECH	DEV Community	Top 8 Most Important Unsupervised Machine Learning Algorithms With Python Code References	https://dev.to/imagescv/top-8-most-important-unsupervised-machine-learning-algorithms-with-python-code-references-egm	Top Most Important Unsupervised Machine Learning Algorithms With Python Code References What are the most important unsupervised machine learning algorithms In this blog post we will list what we believe to be the top Unsupervised machine learning means that there is no predefined outcome or label for any data point during training Without a labeled data set how does one know which algorithm should be used There are many possible answers to this question and it all depends on the type of problem you need to solve The goal of this blog post is to help you figure out which unsupervised machine learning algorithm is best for your problem We will provide a brief overview and examples as well as details about which algorithms are better suited for specific types of data sets Top unsupervised machine learning algorithms include K Means ClusteringThe K means clustering algorithm is one of the most popular unsupervised machine learning algorithms and it is used for data segmentation It works by partitioning a data set into k clusters where each cluster has a mean that is computed from the training data The number of clusters k is usually determined through experimentation The advantage of using the K means clustering algorithm is that it is easy to understand and can be implemented in a short amount of time Additionally it does not require any assumptions about the underlying distribution of the data However there are some disadvantages to using this algorithm First it can be sensitive to initialization values and can result in poor clustering Second it is not scalable to large data sets Lastly it does not work well with categorical data Principal Component Analysis PCA The PCA algorithm is used for dimensionality reduction and is commonly used in conjunction with the k means clustering algorithm It works by finding a lower dimensional space that contains most of the variation in the original data set This can be helpful when working with high dimensional data sets because it reduces the number of dimensions without losing much information Additionally using PCA can improve performance on many machine learning algorithms since they are often sensitive to the dimensionality of the input data set However there are some disadvantages to using PCA First it can be computationally expensive Second it is not always possible to reduce the dimensionality of the data set without losing information Lastly PCA does not work well with categorical data AutoEncoderThe AutoEncoder algorithm is a type of neural network that is used for unsupervised learning It works by taking an input data set and encoding it into a hidden layer The encoded data is then decoded and compared to the original input data set If there is a high degree of similarity between the two sets then the encoder has done its job correctly Otherwise the encoder needs to be tuned until there is a high degree of similarity between the two sets The advantage of using the AutoEncoder algorithm is that it is able to learn complex patterns in data Additionally it is a type of neural network and as such can be trained using backpropagation However there are some disadvantages to using this algorithm First it can be computationally expensive Second if the encoder and decoder are not similar enough then the algorithm will not perform well Lastly it does not work well with categorical data Deep Belief NetworksThe Deep Belief Network DBN algorithm is a deep learning algorithm that is used for unsupervised learning It works by creating a hierarchy of layers where each layer is composed of multiple neurons The first layer is called the input layer and consists of neurons that are connected to the original data set The last layer is called the output layer and it consists of neurons that are used for classification or regression depending on whether supervised learning is required The advantage of using DBNs is that they can be trained quickly since training only occurs in one direction from input to output layers Additionally it works well with problems where there isn t a lot of labeled data available as long as some information about the function being modeled exists However there are some disadvantages to using this algorithm such as its ability to overfit large amounts of training data which limits how much neural networks can improve upon themselves without more labeled training sets Another problem is that these types of deep networks require a large amount of computation to train Lastly it does not work well with categorical data Restricted Boltzmann Machine RBM The Restricted Boltzmann Machine RBM algorithm is a type of neural network that is used for unsupervised learning It works by taking an input data set and splitting it into two parts the visible layer and the hidden layer The visible layer consists of neurons that are connected to the original data set while the hidden layer consists of neurons that are not connected to the original data set The purpose of this algorithm is to learn the relationship between the input and output layers The advantage of using RBM algorithms is that they can be used for dimensionality reduction since they often result in lower dimensional input space Additionally they are able to learn complex relationships between the data set and can be trained quickly since only backpropagation is required for training However there are some disadvantages to using this type of algorithm such as its inability to create more than one hidden layer Another problem is that learning cannot occur with unsupervised pre training methods like AutoEncoders or PCA alone Lastly RBM algorithms do not work well with categorical data Hierarchical Temporal Memory HTM The Hierarchical Temporal Memory HTM algorithm is a type of neural network used for unsupervised learning along with supervised learning problems where labeled examples exist but not enough labels were generated during training time It works by creating a hierarchy of levels where the lower level nodes represent individual pixels and higher level nodes represent object classifications such as face hand or car depending on what is being learned The advantage to using HTM algorithms is that they can be used for unsupervised learning while making predictions about entire sequences rather than just single events like other methods do Additionally it allows hierarchical learning with multiple levels of abstraction which helps in analyzing data sets more efficiently and working with unknown inputs However there are some disadvantages to using this type of algorithm such as long training times compared to traditional neural networks Another problem is that this approach does not work well if each successive layer has fewer processing elements than previous layers since these types of layers will not be able to learn anything Lastly HTM algorithms are unable to handle categorical data Convolutional Neural Networks CNNs Convolutional Neural Networks CNNs are a type of neural network that is used for both unsupervised and supervised learning problems They work by taking an input image and splitting it into small square tiles called windows Each window is then passed through a neuron in the first layer of the CNN which performs a convolution operation on it using a kernel matrix The output of this layer is then passed through another layer of neurons which perform another convolution operation this time with a different kernel matrix This process is repeated until the final layer is reached which produces an output that is a prediction of the input image The advantage to using CNNs is that they are able to learn complex relationships between data sets and can be trained quickly since only backpropagation is required for training Additionally they often result in lower dimensional input spaces than other types of neural networks However there are some disadvantages to using this type of algorithm such as its high computational requirements which can make it difficult to train on large data sets Another problem is that CNNs do not work well with categorical data Support Vector Machines SVMs Support Vector Machines SVMs are a type of machine learning algorithm that is used for both unsupervised and supervised learning problems They work by constructing a hyperplane in a high dimensional space where all the training data points lie on one side of the plane and all the other data points lie on the other side The purpose of an SVM is to find the best possible hyperplane so that it can correctly classify all the training data points The advantage to using SVMs is that they often result in lower dimensional input spaces than other types of machine learning algorithms Additionally they are able to learn complex relationships between data sets and can be trained quickly since only backpropagation is required for training However there are some disadvantages to using this type of algorithm such as its high computational requirements which can make it difficult to train on large data sets Another problem is that SVMs do not work well with categorical data We hope that this list of top most important unsupervised machine learning algorithms has helped you to understand the basics For more information on these algorithms please visit their corresponding Python code references below If there are other popular or trending topics in AI and data science that you would like us to cover let us know images cv provide you with an easy way to build image datasets K categories to choose fromConsistent folders structure for easy parsingAdvanced tools for dataset pre processing image format data split image size and data augmentation Visit images cv to learn more	2022-01-05 11:46:48
海外TECH	DEV Community	SPOTLIGHT: A GENTLE INTRODUCTION TO MACHINE LEARNING CONCEPTS IN PYTHON	https://dev.to/goodnessuc/spotlight-a-gentle-introduction-to-machine-learning-concepts-in-python-3008	SPOTLIGHT A GENTLE INTRODUCTION TO MACHINE LEARNING CONCEPTS IN PYTHONMachine Learning is a branch of Artificial intelligence that deals with the study of computational algorithms and statistical models to perform tasks through patterns and interference instead of explicit tasks The machine Your computer takes in the data and algorithm learns from it and then could use it to predict for other new instances Machine learning is a set of tools used to build models on data that could help predict new types of the same data Machine learning models are now very popular everywhere You must have once wondered how Facebook knew that you know someone or how Spotify got that really cool jam recommended to you Well that is machine learning and the same way Facebook could pop up someone you do not know is based on the fact that machine learning models are not accurate all the time Machine Learning comprises of supervised learning where we know the target or past answer and unsupervised learning where there are no targets In this article we will be covering the basics of machine learning by viewing the basic ways machine learning models carry out operations to give us juicy feedback This article is on supervised learning as this is an introduction The Concepts below cover the basics of machine learning GROUPING DATA INTO FEATURES AND TARGETS As highlighted earlier in supervised learning we aim at predicting values based on past data target hence the dataset with which we work comes with a column which contains the values which we are trying to predict This is the target column Whilst other columns which are going to be used to predict the target are called the features Datasets could be ambiguous containing useless data which we would want to get rid of and here data analysis and manipulation comes in handy We would also want to have a good knowledge of the features and we can do this with a good knowledge of exploratory data analysis Here is a link to an article on exploratory data analysis article you might find useful BASIC MACHINE LEARNING ALGORITHM OPERATIONSClassification In classification machine learning models group data into different parts based on the algorithm provided Popular classification algorithms include the K Nearest Neighbors Support Vector Machines amongst others and with the help of a concept called cross validation we will be able to pick the best one to work with on our data Regression Regression algorithms operate by giving out the relationship between two or more features in our model Examples are linear and logistic regression algorithms Regression and classification are categorized under the same umbrella of supervised machine learning  …The main difference between them is that the output variable in regression is numerical or continuous while that for classification is categorical or discrete THE MACHINE LEARNING WORKFLOWImporting By importing we get the necessary tools we are going to be using on our machine learning model examples are the algorithms and the tools we use for exploratory data analysisfrom sklearn linearmodel import LinearRegressionInstantiating This is the process of the creation of an instance of the machine learning method While some of them accept parameters like the k nearest neighbours others do not accept parameters my model LinearRegression Splitting into training and testing data We split the data into training and testing sets We go ahead to work with the training set and then compare them with the testing set to have a glance at how well our model performed from sklearn model selection import train test splitX feature columnsy target columnX train X test y train y test train test split X y test size train test split does a tuple unpacking of the data The argument as seen above test size is the portion of the data we are willing to allocate to the machine learning method for training Fit By fitting a model we are feeding the training set of our data to the algorithm to operate on We could tune how the algorithm operates of the fitted data We would fit the training data to the machine learning method my model fit X train y train Predict The goal of using a machine learning algorithm is to be able to get predictions and feedbacks off of it and we do this using the predict method of the machine learning algorithm Most machine learning algorithms are already sophisticated and ready to use so most of the work a data scientist does is with refining the data for the algorithm We predict on the X test from the train test split tuple unpacking prediction my model predict X test EVALUATING A MODEL S PERFORMANCEClassification report A classification report is a metric that gives a table of how well the algorithm performed in percentages The table contains the precision recall and the f score The precision column tells us the percentage score of how well the algorithm classified our model accurately The recall column gives us feedback on how the algorithm classified data that did not belong to a category while the f score is a harmonic mean of the precision and recall A classification report comes in handy for model evaluation as it is simple to obtain from sklearn metrics import classification reportprint classification report y test predictions The confusion matrix The confusion matrix method is used to summarize how the algorithm performed on our data In a confusion matrix the rows correspond to values the algorithm predicted while the columns correspond to the known truths actuals Values on the diagonal specify where the algorithm correctly classified our data while the others show where the algorithm failed Using the confusion matrix we can compare how well different algorithms perform on our data and then go ahead to select the algorithm that best suits our data from sklearn metrics import confusion matrixprint confusion matrix y test predictions Cross Validation Cross Validation allows us to compare different machine learning methods and get a sense of how well they will work in practice which helps us choose the machine learning algorithm that best suits our data Cross validation does this by splitting the data into training and testing sets Splitting the data into n numbers the type of cross validation is the n cross validation since the number of splits is arbitrary Cross validation uses every part of the split data to deliver Bias and Variance Bias is the inability of a machine learning method to capture the true relationship between features Variance is the difference in fits between different datasets In machine learning a good algorithm is one with a low bias can accurately model the true relationship and has low variability it should be able to provide consistently good predictions over different datasets When there is a case of a high bias and low variance the model is under fitted and when there are a low bias and a high variance the model is over fit Trading bias for variance and vice versa is a Bias Variance Trade off With knowledge of these basic concepts learning other machine learning methods would be quite easy Follow and share this article if you find it useful Till I get into your mind on this app next time ‍	2022-01-05 11:24:37
海外TECH	DEV Community	Virtual Background in Android with WebRTC	https://dev.to/100mslive/virtual-background-in-android-with-webrtc-5720	Virtual Background in Android with WebRTCVirtual backgrounds are becoming necessary nowadays in the video conferencing world It allows us to replace our natural background with an image or a video We can also upload our custom images in the background In this blog we are going to implement Virtual Background in Android with WebRTC using mlkit selfie segmentation This content was originally published HEREThis feature works best with uniform lightning condition in background and requires a high performance mobile android device for a smooth user experience By end of this blog you can expect the virtual background feature to look like this DependenciesAdd the dependencies for the ML Kit Android libraries to the module s app level gradle file which is usually app build gradle dependencies implementation com google mlkit segmentation selfie beta Add the dependencies for the libyuv dependencies implementation io github zncmn libyuv core libyuv is an open source project that includes YUV scaling and conversion functionality Common WebRTC terms you should knowVideoFrame It contains the buffer of the frame captured by the camera device in I format VideoSink It is used to send the frame back to WebRTC native source VideoSource It reads the camera device produces VideoFrames and deliver them to VideoSinks VideoProcessor It is an interface provided by WebRTC to update videoFrames produced by videoSource MediaStream It is an API related to WebRTC which provides support for streaming audio and video data It consists of zero or more MediaStreamTrack objects representing various audio or video tracks Approaches we thought ofUpdating the WebRTC MediaStream by passing it to the mlkit selfie segmentation model and getting the updated stream But sadly we don t have a method in android to replaceTrack in WebRTC Updating the stream coming from the source camera and then passing it to WebRTC Got some success on it but then issues were faced in using the updated stream in the WebRTC Creating another virtual video source from the camera source and using that as an input to mlkit API But sending the updated stream back to WebRTC gave us issues Using Android CameraX Apis to read frames but again WebRTC doesn t support it After trying all these approaches and not getting suitable results we figured out that we need to do processing on VideoFrame for our use case Getting the VideoFrame from WebRTCMost challenging part was getting the VideoFrame out for every frame from WebRTC for processing After a lot of research we found out that we can use setVideoProcessor API available with VideoSource It has few callbacks It gives us VideoFrame going into WebRTC for every framefun onFrameCaptured inputVideoFrame VideoFrame It gives us sink which we will use to send updated videoFrame back to WebRTCfun setSink sink VideoSink This is how we can setVideoProcessor to VideoSource source in below code snippet is VideoSource source setVideoProcessor object VideoProcessor override fun onCapturerStarted p Boolean override fun onCapturerStopped override fun onFrameCaptured inputVideoFrame VideoFrame Do processing with inputVideoFrame here override fun setSink sink VideoSink set sink here to send updated videoFrame back to WebRTC If we are setting VideoProcessor to the VideoSource we need to call onFrame callback on every frame from VideoSink otherwise we will get a black screen on our device Here frame is the updated VideoFrame we are getting after ML processing on input videoFramesink onFrame frame Converting VideoFrame to supported ML model Input TypeTo perform segmentation on an image mlkit needs an InputImage object which can be created from either a bitmap bytebuffer media Image byte array or a file on the device Here we have converted inputVideoFrame into a bitmap using libyuv libraryYuvFrame It copies the Y V and U planes from videoFrame buffer into a byte array which we are converting to ARGB BitmapyuvFrame YuvFrame inputVideoFrame YuvFrame PROCESSING NONE inputVideoFrame timestampNs inputFrameBitmap yuvFrame bitmapNow we have created InputImage using inputFrameBitmapval mlImage InputImage fromBitmap inputFrameBitmap Initialise mlkit modelWe have created an instance of Segmenter using this Process the mlImagesegmenter process mlImage addOnSuccessListener segmentationMask gt val mask segmentationMask buffer val maskWidth segmentationMask width val maskHeight segmentationMask height mask rewind val arr IntArray maskColorsFromByteBuffer mask maskWidth maskHeight val segmentedBitmap Bitmap createBitmap arr maskWidth maskHeight Bitmap Config ARGB segmentedBitmap is the person segmented from background addOnFailureListener exception gt HMSLogger e App exception message addOnCompleteListener Draw the segmented background on the canvasWe have used Porter Duff mode to draw segmented output with the background image given by user on the Canvas using canvas APIs After this we will get outputBitmap from canvas which we are using to create an updated VideoFrame Create new VideoFrame from outputBitmapsurfTextureHelper handler post GLES glTexParameteri GLES GL TEXTURE D GLES GL TEXTURE MIN FILTER GLES GL NEAREST GLES glTexParameteri GLES GL TEXTURE D GLES GL TEXTURE MAG FILTER GLES GL NEAREST GLUtils texImageD GLES GL TEXTURE D outputBitmap val iBuf yuvConverter convert inputBuffer val outputVideoFrame VideoFrame iBuf frameTs is the frame rotation degree which we are using Send VideoFrame back to WebRTCThis will replace the input video feed with the background supplied on both local and remotesink onFrame outputVideoFrame Time takenThe whole pipeline takes less than ms per frame on One Plus Android OptimizationsMost of the processing time is taken by input VideoFrame to YuvFrame conversion Since the real time view doesn t change much on every frame there is no point in doing this conversion on every frame The previous converted YuvFrame can be easily used for processing It helps in enhancing the performance and user experience	2022-01-05 11:10:12
Apple	AppleInsider - Frontpage News	Nanoleaf products will soon act as Thread border routers for HomeKit devices	https://appleinsider.com/articles/22/01/05/nanoleaf-products-will-soon-act-as-thread-border-routers-for-homekit-devices?utm_medium=rss	Nanoleaf products will soon act as Thread border routers for HomeKit devicesDuring CES Nanoleaf announced a forthcoming firmware update that will allow its products to act as Thread border routers to all HomeKit over Thread devices Nanoleaf products can now act as Thread border routers for HomeKitExpected to roll out in Q of the new firmware update broadens support for Thread beyond what Nanoleaf s products are currently capable of Read more	2022-01-05 11:41:21
Apple	AppleInsider - Frontpage News	Vanessa Kirby replaces Jodie Comer in Ridley Scott's 'Kitbag'	https://appleinsider.com/articles/22/01/05/vanessa-kirby-replaces-jodie-comer-in-ridley-scotts-kitbag?utm_medium=rss	Vanessa Kirby replaces Jodie Comer in Ridley Scott x s x Kitbag x Ridley Scott s forthcoming Apple TV movie Kitbag about the life of Napoleon will now feature Mission Impossible star Vanessa Kirby Vanessa Kirby in Mission Impossible Jodie Comer has left the production of Kitbag following scheduling issues She has been replaced by Vanessa Kirby best known for Mission Impossible and her Oscar nominated performance in Pieces of a Woman Read more	2022-01-05 11:36:35
Apple	AppleInsider - Frontpage News	Apple VR headsets still scheduled for 2022, but will be very hard to get	https://appleinsider.com/articles/22/01/05/apple-ar-supplier-to-make-lenses-for-oculus-quest-3-says-kuo?utm_medium=rss	Apple VR headsets still scheduled for but will be very hard to getWhile Apple s VR headset may be released in supply constraints and other issues will mean that it will only be available in limited quantities until analyst Ming Chi Kuo reports Reiterating his previous claim that Apple has pushed production of its first headset to the end of Ming Chi Kuo says this will give supplier Genius an advantage Genius is one of two firms that Apple has ordered pancake Fresnel lenses from although as yet its production yield is less than that of rival Young Optics In a note to investors seen by AppleInsider Kuo says that Apple s AR MR production delays will help Genius close the gap in production yields for pancake lenses with Young Optics Read more	2022-01-05 11:14:51
ニュース	BBC News - Home	Sir Keir Starmer tests positive for Covid for a second time	https://www.bbc.co.uk/news/uk-politics-59880263?at_medium=RSS&at_campaign=KARANGA	angela	2022-01-05 11:15:14
ニュース	BBC News - Home	Novak Djokovic: Australian Open vaccine exemption ignites backlash	https://www.bbc.co.uk/news/world-australia-59876203?at_medium=RSS&at_campaign=KARANGA	locals	2022-01-05 11:49:30
ニュース	BBC News - Home	Covid: PCR not needed after positive lateral flow under new plans	https://www.bbc.co.uk/news/uk-59878823?at_medium=RSS&at_campaign=KARANGA	covid	2022-01-05 11:21:49
ニュース	BBC News - Home	Army officer completes remarkable solo South Pole trek	https://www.bbc.co.uk/news/uk-england-derbyshire-59869042?at_medium=RSS&at_campaign=KARANGA	comfort	2022-01-05 11:00:52
ニュース	BBC News - Home	Liverpool shut training facilities for 'at least 48 hours' with Lijnders latest to isolate	https://www.bbc.co.uk/sport/football/59878393?at_medium=RSS&at_campaign=KARANGA	Liverpool shut training facilities for x at least hours x with Lijnders latest to isolateLiverpool temporarily close their first team training centre amid a rapidly growing number of suspected Covid cases with assistant manager Pep Lijnders the latest to have to isolate	2022-01-05 11:49:15
ニュース	BBC News - Home	Adelaide International: Ashleigh Barty fights back to beat Coco Gauff	https://www.bbc.co.uk/sport/tennis/59811349?at_medium=RSS&at_campaign=KARANGA	adelaide	2022-01-05 11:19:25
北海道	北海道新聞	函館・五島軒、パン製造販売へ　「原点回帰」の新事業７月にも	https://www.hokkaido-np.co.jp/article/630297/	原点回帰	2022-01-05 20:17:34
北海道	北海道新聞	取引先の本業支援強化、新部署も　道内２７信金・信組	https://www.hokkaido-np.co.jp/article/630348/	信用組合	2022-01-05 20:16:00
北海道	北海道新聞	「コロナ退散」願い打ち初め　室蘭の日本製鋼所	https://www.hokkaido-np.co.jp/article/630345/	室蘭製作所	2022-01-05 20:12:00
北海道	北海道新聞	中国主席ら政府当局者を刑事告発　亡命ウイグル族、トルコ検察に	https://www.hokkaido-np.co.jp/article/630344/	刑事告発	2022-01-05 20:06:00
北海道	北海道新聞	ラグビーのリーグワン開幕戦中止　コロナ陽性者６人確認で	https://www.hokkaido-np.co.jp/article/630343/	陽性	2022-01-05 20:06:00
ビジネス	東洋経済オンライン	中国鉱業大手｢アフリカ資源｣の物流改善に布石 紫金鉱業､コンゴに権益持つ嘉友国際に出資 | 「財新」中国Biz＆Tech | 東洋経済オンライン	https://toyokeizai.net/articles/-/479353?utm_source=rss&utm_medium=http&utm_campaign=link_back	国際貨物輸送	2022-01-05 20:30:00
海外TECH	reddit	You can get rid of ONE food, never to return. What would it be?	https://www.reddit.com/r/AskReddit/comments/rwkcf4/you_can_get_rid_of_one_food_never_to_return_what/	You can get rid of ONE food never to return What would it be submitted by u SASHTv to r AskReddit link comments	2022-01-05 11:00:52