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04 classfication new. 支持向量機實例講解 - 壹讀. 簡介 掌握機器學習算法不再是天方夜譚的事情。

支持向量機實例講解 - 壹讀

大多數初學者都是從回歸模型學起。

SampleData

智慧搜尋. 推薦系統. 智慧製造. 深度學習. Feature Selection. Cortana Intelligence and Machine Learning Blog. Haven OnDemand. 10.1. Datasets. Datasets New Dataset Welcome to Kaggle Datasets The best place to discover and seamlessly analyze open data DiscoverUse the search box to find open datasets on everything from government, health, and science to popular games and dating trends.ExploreExecute, share, and comment on code for any open dataset with our in-browser analytics tool, Kaggle Kernels.

Datasets

You can also download datasets in an easy-to-read format.Create a DatasetContribute to the open data movement and connect with other data enthusiasts by clicking “New Dataset” to publish an open dataset of your own. Dismiss 292 featured datasets Sort By Hotness Human Resources Analytics Why are our best and most experienced employees leaving prematurely?

Ludovic Benistant · updated a month ago 5,146 downloads 173 kernels 32 comments.

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Setup CNTK on your machine · Microsoft/CNTK Wiki. CNTK Setup The Microsoft Cognitive Toolkit (CNTK) supports both 64-bit Windows and 64-bit Linux platforms.

Setup CNTK on your machine · Microsoft/CNTK Wiki

You can install the complete source code of CNTK and build the binaries on your machine, but we also provide regular binary drops of the CNTK executables, including sample data and sample models. Binary Installation of CNTK If you just want to download and install the latest precompiled binaries to your machine, follow the instructions here: Installation and building of the CNTK codebase If you want to take a look at the CNTK source code, compile CNTK yourself, make changes to the CNTK codebase, and contribute these changes back to the community, these are the pages for you: CNTK/Examples/Text/PennTreebank at master · Microsoft/CNTK. Sakananote: 安裝 Spark in Ubuntu 12.04 小記. 最近由於研究需要 要開始研究 Apache Spark 先從大家常安裝的 Ubuntu 12.04 開始 之後來研究 CentOS 與 openSUSE 安裝 Spark in Ubuntu 12.04 OS: Ubuntu 12.04 LTS.

sakananote: 安裝 Spark in Ubuntu 12.04 小記

用十分鐘瞭解 《人工智慧的那些問題與方法》 GitHub - mumian/HDInsight-Labs-Preview: This repository includes 5 hands on labs in preview. MapReduce, Chain multiple MR jobs together, data import and Hive, PivotTable&PivotChart, Collaborative Filtering. MapR Academy. Free and Open Source Software for Education.

TensorFlow

排程. Azure 「機器學習」: 初探類神經網路 (Neural Network) - Meng-Ru Tsai's Blog. Does Zeppelin works with Spark stream? StringSeqToProcess /** Configures the Oauth Credentials for accessing Twitter */def configureTwitterCredentials(apiKey: String, apiSecret: String, accessToken: String, accessTokenSecret: String) { val configs = new HashMap[String, String] ++= Seq( "apiKey" -> apiKey, "apiSecret" -> apiSecret, "accessToken" -> accessToken, "accessTokenSecret" -> accessTokenSecret) println("Configuring Twitter OAuth") configs.foreach{ case(key, value) => if (value.trim.isEmpty) { throw new Exception("Error setting authentication - value for " + key + " not set") } val fullKey = "twitter4j.oauth.

Does Zeppelin works with Spark stream?

" + key.replace("api", "consumer") System.setProperty(fullKey, value.trim) println("\tProperty " + fullKey + " set as [" + value.trim + "]") } println()} // Configure Twitter credentialsval apiKey = ""val apiSecret = ""val accessToken = ""val accessTokenSecret = ""configureTwitterCredentials(apiKey, apiSecret, accessToken, accessTokenSecret) import org.apache.spark.streaming.twitter. twt.print ssc.start() Machine Learning Foundations (機器學習基石) Upload Hsuan-Tien Lin Loading...

Machine Learning Foundations (機器學習基石)

Working... ► Play all. 機器學習基石 (Machine Learning Foundations) - National Taiwan University. About the Course Welcome!

機器學習基石 (Machine Learning Foundations) - National Taiwan University

The instructor has decided to teach the course in Mandarin on Coursera, while the slides of the course will be in English to ease the technical illustrations. We hope that this choice can help introduce Machine Learning to more beginners in the Mandarin-speaking world. The English-written slides will not require advanced English ability to understand, though. If you can understand the following descriptions of this course, you can probably follow the slides. Machine learning is an exciting field with lots of applications in engineering, science, finance, and commerce. 大數學堂 - 最好的大數據分析課程網站. Regex - Removing punctuation marks form text in Scala - Spark. Hadoop+Spark大數據巨量分析與機器學習整合開發實戰.

LIBSVM Data: Classification, Regression, and Multi-label. This page contains many classification, regression, multi-label and string data sets stored in LIBSVM format.

LIBSVM Data: Classification, Regression, and Multi-label

Many are from UCI, Statlog, StatLib and other collections. We thank their efforts. For most sets, we linearly scale each attribute to [-1,1] or [0,1]. Classification and Regression - MLlib - Spark 1.5.2 Documentation. Reading data from SQL Server using Spark SQL. PairRDDFunctions - org.apache.spark.rdd.PairRDDFunctions. XML To CSV Converter. Test Data Generation. Machine Learning Studio:演算法和模組說明. 在 Machine Learning Studio 中建立簡易實驗. 在第一個機器學習教學課程中,我們將建立一個線性迴歸模型,該模型會根據使用製造和技術規格等不同變數,來預測汽車的價格。

在 Machine Learning Studio 中建立簡易實驗

如何在 Machine Learning 中解譯模型結果. 了解和視覺化「評分模型」輸出本主題說明如何視覺化和解譯 Azure Machine Learning Studio 中的預測結果。

如何在 Machine Learning 中解譯模型結果

在您訓練模型並且完成模型最上層的預測 (「評分模型」) 之後,您必須了解和解譯您已取得的預測結果。 Microsoft Azure. 「我該使用何種機器學習演算法?」 的答案永遠都是「視情況。」 這可視資料的大小、品質和本質而定。 也可取決於您想要的答案。 或是取決於演算法的數學運算如何針對您正在使用的電腦轉譯成指令。 而這又需視您有多少時間。 機器學習演算法小祕技. 機器學習服務 - 建置進階分析解決方案. Descriptive Statistics. Rdatasets/datasets.csv at master · vincentarelbundock/Rdatasets. Vincentarelbundock (Vincent Arel-Bundock) Microsoft Azure. 您可以利用執行 R 指令碼模組,透過 R 語言擴充 ML Studio 的功能。 此模組接受多個輸入資料集,並產生單一資料集作為輸出。 您可以將 R 指令碼輸入至執行 R 指令碼模組的 [R 指令碼] 參數。 您可使用類似下面的程式碼,存取模組的每個輸入連接埠: Copy to clipboardCopy.

機器學習模組. R 語言初體驗- 使用於 Azure Machine Learning Studio 中 - Meng-Ru Tsai's Blog. “R 語言,一種自由軟體程式語言,主要用於統計分析、繪圖及資料探勘。” 維基百科: R 語言 本文將帶您檢視 410 個預裝的 R packages、展示如何直接使用既有的 R script,或直接在 Azure ML 中撰寫、透過 R 作資料清理進而建立預測模型、以及如何極大化 R 語言在「機器學習」上的應用。 在 Machine Learning Studio 中建立簡易實驗.