MicrosoftML (MML) is a new machine learning package for Microsoft R Server. Microsoft R Server brings you the ability to do parallel and chunked data processing that addresses the restrictions of in-memory open source R. MML adds Microsoft's battle-tested algorithms and data transforms that are used by product teams across Microsoft. This brings new machine learning functionality with increased speed, performance and scale, especially for handling a large corpus of text data and high-dimensional categorical data. MML includes the ability to: Create text classification models for problems such as sentiment analysis and support ticket classification. Train deep neural nets in order to solve complex problems such as retail image classification and handwriting analysis. Work with high-dimensional data for scenarios like online advertising click-through prediction. Solve many other common machine learning tasks such as churn prediction, loan risk analysis, and demand forecasting ...
Machine Learning Service (R,Python )in SQL Server 2017 SQL Server 2017 Machine Learning Services is an add-on to a database engine instance, used for executing R and Python code on SQL Server. The feature includes microsoft R python packages for high-performance predictive analytics and machine learning. Code runs in an extensibility framework, isolated from core engine processes, but fully available to relational data as stored procedures, as T-SQL script containing R or Python statements, or as R or Python code containing T-SQL. If you previously used , Machine Learning SQL server 2016 R service in SQL Server 2017 is the next generation of R support, with updated versions of base R, RevoScaleR, MicrosoftML, and other libraries introduced in 2016. In Azure SQL Database machine learning service with R is currently in public preview. Bring compute power to the data The key value proposi...
Optimizations in Syntax Highlighting Visual Studio Code version 1.9 includes a cool performance improvement that we've been working on and I wanted to tell its story. TL;DR TextMate themes will look more like their authors intended in VS Code 1.9, while being rendered faster and with less memory consumption. Syntax Highlighting Syntax Highlighting usually consists of two phases. Tokens are assigned to source code, and then they are targeted by a theme, assigned colors, and voilĂ , your source code is rendered with colors. It is the one feature that turns a text editor into a code editor. Tokenization in VS Code (and in the Monaco Editor ) runs line-by-line, from top to bottom, in a single pass. A tokenizer can store some state at the end of a tokenized line, which will be passed back when tokenizing the next line. This is a technique used by many tokenization engines, including TextMate grammars, that allows an editor to retokenize only a small subset of the line...
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