投稿時間:2022-12-25 07:09:56 RSSフィード2022-12-25 07:00 分まとめ(12件)

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Google	カグア！Google Analytics 活用塾:事例や使い方	WRC ジェネレーションズが難しい人もこうすると上達するコツ	https://www.kagua.biz/playgame/gamelife/20221225a1.html	練習	2022-12-24 21:00:31
海外TECH	MakeUseOf	How to Increase Dedicated Video RAM (VRAM) in Windows 10 and 11	https://www.makeuseof.com/tag/video-ram-windows-10/	dedicated	2022-12-24 21:30:15
海外TECH	MakeUseOf	How to Create Shortcuts to Anything on Your Android Phone's Home Screen	https://www.makeuseof.com/create-shortcuts-android-shortcut-maker-app/	How to Create Shortcuts to Anything on Your Android Phone x s Home ScreenYou don t have to limit yourself to just having apps on your home screen With Shortcut Maker you can create shortcuts to almost anything	2022-12-24 21:30:15
海外TECH	MakeUseOf	8 Underrated Xbox Series X|S Features You Should Try	https://www.makeuseof.com/underrated-xbox-series-x-s-features-you-should-try/	haven	2022-12-24 21:15:15
海外TECH	DEV Community	Time series forecasting of stock data using a deep learning model.	https://dev.to/anurag629/time-series-forecasting-of-stock-data-using-a-deep-learning-model-16ke	Time series forecasting of stock data using a deep learning model To forecast stock data using a deep learning model we will follow the following steps Collect and pre process the data We will first need to collect the stock data for the time period we want to forecast This can be done by accessing financial databases or by manually collecting the data from sources such as stock exchange websites Next we will pre process the data by cleaning and normalizing it This may include removing any missing or corrupted data as well as scaling the data to make it easier for the model to process Build the deep learning model Once the data has been pre processed we will build the deep learning model using a neural network architecture This may include selecting the type of model such as a recurrent neural network or a convolutional neural network and determining the number and size of the layers We will also need to determine the optimal hyperparameters for the model such as the learning rate and the number of epochs Train the model Once the model has been built we will train it using the pre processed data This will involve feeding the data into the model and adjusting the weights and biases to optimize the model s performance Test the model After the model has been trained we will need to test its performance on a separate dataset to ensure that it is able to accurately predict future stock prices Make predictions Once the model has been trained and tested we can use it to make predictions on future stock data This may involve inputting new data into the model and using the output to make informed decisions about buying and selling stocks As an example let s say we want to forecast the stock price of Company X for the next month using a deep learning model Here are the steps we would follow Collect and pre process the data We collect the stock data for Company X for the past year and pre process it by cleaning and normalizing the data Build the deep learning model We decide to use a recurrent neural network as our model with two hidden layers and a learning rate of We also determine that we will train the model for epochs Train the model We feed the pre processed data into the model and train it using the specified hyperparameters Test the model We test the model s performance on a separate dataset and find that it can accurately predict stock prices with an error rate of Make predictions We input new data into the model and use the output to make informed decisions about buying and selling Company X stocks in the next month Here is an example of code that can be used to forecast stock data using a deep learning model with CSV data First we will import the necessary libraries and read the CSV data You will find this data in the Kaggle dataset in the following link Stock Market daily dataimport pandas as pdimport numpy as npfrom sklearn preprocessing import MinMaxScalerfrom keras models import Sequentialfrom keras layers import Dense LSTM Read in the CSV datadf pd read csv stock data csv Next we will pre process the data by cleaning and normalizing it Convert the Date column to datetime objectsdf Date pd to datetime df Date Extract the year month and day as separate columnsdf Year df Date dt yeardf Month df Date dt monthdf Day df Date dt day Drop the original Date columndf df drop columns Date Scale the datascaler MinMaxScaler feature range df scaled scaler fit transform df Split the data into training and testing setstrain size int len df scaled test size len df scaled train sizetrain test df scaled train size df scaled train size len df scaled Convert the data into a D array a sequence with t timesteps and d dimensions def create sequences data t d X y for i in range len data t a data i i t X append a y append data i t return np array X np array y Create sequences of t timesteps with d dimensionst timestepsd dimensions including year month and day X train y train create sequences train t d X test y test create sequences test t d Then we will build and train the deep learning model Build the modelmodel Sequential model add LSTM input shape t d model add Dense d model compile loss mean squared error optimizer adam Train the modelhistory model fit X train y train epochs batch size verbose Finally we will test the model and make predictions Test the modeltest error model evaluate X test y test verbose print f Test error test error print f Accuracy test error Hope you liked it Sharing knowledge and love	2022-12-24 21:26:49
海外TECH	DEV Community	MongoDB cheat sheet	https://dev.to/arafat4693/mongodb-cheat-sheet-2f9o	MongoDB cheat sheetHere is a cheat sheet for mongodb Basic IdeaDatabase A container of collection Collection Grouping of documents insida of a database Similar tables in SQL Document A record inside of a collection Similar to row in SQL Field A key value pair within a document Similar to column in SQL Basic commandsmongosh A JavaScript shell for interacting with MongoDB instances It provides a command line interface CLI that allows you to connect to a MongoDB server show dbs Shows all databases in the current MongoDB instance use lt dbname gt Switch database provided by dbname db Shows current database name show collections Shows all collections db dropDatabase Deletes the current database exit Exits the mongosh session CreateinsertOne Creates a document within the specified collection db users insertOne name “Arafat Create a document with the name of Arafat into the users collectioninsertMany Creates multiple documents within the specified collection db users insertMany name “John age “Roy Create two documents with the name John and Roy into the users collection Readfind Get all documents db users find find lt filterObject gt Find all documents based on the filter objectdb users find name “Arafat Get all users with the name Arafatdb users find “address street “ Lund Sweden Get all users whose adress field has a street field with the value Lund Swedenfind lt filterObject gt lt selectObject gt Find all documents that match the filter object but only return the field specified in the select objectdb users find name “Arafat name hobby Get all users with the name Arafat but only return their name hobby and iddb users find hobby Get all users and return all fields except for hobbyfindOne Returns the first document that matches the filter object db users findOne name “Arafat Get the first user with the name ArafatcountDocuments Returns the count of the documents that match the filter object db users countDocuments name “Arafat Get the number of users with the name Arafat UpdateupdateOne Updates the first document db users updateOne name Arafat set name Theo Update the first user with a name of Arafat to the name of TheoupdateMany Updates multiple docments db users updateMany age inc age Update all users with an age of by adding to their agereplaceOne Replace the first document Thiswill completely overwrite the entire object and not justupdate individual fields db users replaceOne age age Replace the first user with an age of with an object that has the age of as its only field DeletedeleteOne Delete a single document from a collection db users deleteOne name Arafat Delete the first user with an name of ArafatdeleteMany Delete multiple documents from a collection db users deleteMany age Delete all users with an age of Complex Filter Object eq equals db users find name eq “Arafat Get all the users with the name Arafat ne not equal to db users find name ne “Arafat Get all users with a name other than Kyle gt gte Greater than and greater than or eqal db users find age gt Get all users with an age greater than db users find age gte Get all users with an age greater than or equal to lt lte Less than and less than or eqal db users find age lt Get all users with an age less than db users find age lte Get all users with an age less than or equal to in Check if a value is one of many values db users find name in “Roy “Leo Get all users with a name of Roy or Leo nin Check if a value is none of many values db users find name nin “Roy “Leo Get all users that do not have the name Roy or Leo and Returns true if all expressions are truedb users find and age name “Arafat Get all users that have an age of and the name Arafatdb users find age name “Arafat Alternative way to do same thing or returns true if any expression is truedb users find or age name “Arafat Get all users that have an age of or the name Arafat not Negates the expressiondb users find name not eq “Arafat Get all users with a name other than Arafat exists Matches documents that have the specified field db users find name exists true Returns all users that have a name field expr performs an expression evaluation in the query db users find expr gt “ balance “ debt Get all users that have a balance that is greater than their debt Complex Update Object set Updates only the fields passed to set db users updateOne age set name “Roy Update the name of the first user with the age of to the value Roy inc Increments the value of a field by a specified amount db users updateOne debt inc debt Add to the debt of the first user with the debt of db users updateOne debt inc debt Remove from the debt of the first user with the debt of rename Rename a fielddb users updateMany rename loss “Profit Rename the field loss to profit for all users unset Remove a field db users updateOne age unset age Remove the age field from the first user with an age of push Adds new elements to an arraydb users updateMany push enemy “Ria Add Ria to the enemy array for all users pull Rmoves all array elements that match a specified condition db users updateMany pull enemy “Arafat Remove Mike from the friends array for all users Modifiers for readsort Sort the results of a find by the given fields db users find sort debt balance Returns all users sorted by name in alphabetical order and then if any duplicated names exits sorts by age in reverse order limit Returns a specified number of documents db users find limit Returns the first usersskip Skip a specified number of documents from the start db users find skip Skip the first users when returning results Freat for pagination when combined with limit	2022-12-24 21:04:13
海外科学	NYT > Science	A Cathedral Tried to Approach Heaven, but the Earth Held a Deep Secret	https://www.nytimes.com/2022/12/23/science/cathedral-st-john-divine-nyc.html	A Cathedral Tried to Approach Heaven but the Earth Held a Deep SecretFlows of spring water cut the planned height of St John the Divine in half and prompted studies to illuminate the church s watery past and future	2022-12-24 21:01:11
ニュース	BBC News - Home	Afghanistan: Taliban bans women from working for NGOs	https://www.bbc.co.uk/news/world-asia-64086682?at_medium=RSS&at_campaign=KARANGA	codes	2022-12-24 21:33:11
北海道	北海道新聞	米、歴史的寒波で被害拡大　１６人死亡、クリスマスイブ	https://www.hokkaido-np.co.jp/article/780352/	被害	2022-12-25 06:26:45
北海道	北海道新聞	サッカー森保監督、続投へ　次回Ｗ杯に向け要請を受諾	https://www.hokkaido-np.co.jp/article/780353/	日本代表	2022-12-25 06:06:00
ビジネス	東洋経済オンライン	NHK｢過去最大の改定率｣に見る若者離れの危機感 若者向けゾーン設置や夜ドラ等さまざまな施策 | テレビ | 東洋経済オンライン	https://toyokeizai.net/articles/-/640378?utm_source=rss&utm_medium=http&utm_campaign=link_back	引き下げ	2022-12-25 06:20:00
海外TECH	reddit	Like it’s hard?	https://www.reddit.com/r/NFCEastMemeWar/comments/zuisxy/like_its_hard/	Like it s hard submitted by u DaddyShark to r NFCEastMemeWar link comments	2022-12-24 21:06:50