Search Results for "virtual windows machine"
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Showing 8 open source projects for "virtual windows machine"
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Pandas Profiling
Create HTML profiling reports from pandas DataFrame objects
pandas-profiling generates profile reports from a pandas DataFrame. The pandas df.describe() function is handy yet a little basic for exploratory data analysis. pandas-profiling extends pandas DataFrame with df.profile_report(), which automatically generates a standardized univariate and multivariate report for data understanding. High correlation warnings, based on different correlation metrics (Spearman, Pearson, Kendall, Cramér’s V, Phik). Most common categories (uppercase, lowercase,... -
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quick-xml
Rust high performance xml reader and writer
High-performance xml pull reader/writer. The reader is almost zero-copy (use of Cow whenever possible) It is easy on memory allocation (the API provides a way to reuse buffers) It supports various encoding (with encoding feature), namespaces resolution, and special characters. Syntax is inspired by xml-rs. When using the serialize feature, quick-xml can be used with serde's Serialize/Deserialize traits. The mapping between XML and Rust types, and in particular the syntax that allows you to... -
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Nokogiri
Tool to work with XML and HTML from Ruby
Nokogiri (鋸) makes it easy and painless to work with XML and HTML from Ruby. It provides a sensible, easy-to-understand API for reading, writing, modifying, and querying documents. It is fast and standards-compliant by relying on native parsers like libxml2 (C) and xerces (Java). Be secure-by-default by treating all documents as untrusted by default. Be a thin-as-reasonable layer on top of the underlying parsers, and don't attempt to fix behavioral differences between the parsers. "Native... -
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hyperx
Tagged template string virtual dom builder
tagged template string virtual dom builder. This module is similar to JSX, but provided as a standards-compliant ES6 tagged template string function. hyperx works with virtual-dom, react, hyperscript, or any DOM builder with a hyperscript-style API: h(tagName, attrs, children). You might also want to check out the hyperxify browserify transform to statically compile hyperx into javascript expressions to save sending the hyperx parser down the wire. Template strings are available in: node 4+,... -
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Myrtille
A native HTML4 / HTML5 Remote Desktop Protocol and SSH client
Myrtille provides simple and fast access to remote desktops, applications, and SSH servers through a web browser, without any plugin, extension or configuration. Technically, Myrtille is an HTTP(S) to RDP and SSH gateway. User input (keyboard, mouse, touchscreen) is forwarded from a web browser to an HTTP(S) gateway, then up to an RDP (or SSH) client which maintains a session with an RDP (or SSH) server. The display resulting (or not) of such actions is streamed back to the browser, from the... -
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unfluff
Automatically extract body content (and other cool stuff) from HTML
unfluff is a Node.js library designed to automatically extract the main content from an HTML document — stripping away navigation bars, ads, footers and other boilerplate to leave you with the “body content”, metadata (title, author, date) and other useful fields. It’s a tool very much aimed at content-analysis, web scraping, building datasets, or repurposing article text for downstream processing (like machine-learning or summarization). The API is simple: you feed in raw HTML and it... -
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DeepLearningProject
An in-depth machine learning tutorial
This tutorial tries to do what most Most Machine Learning tutorials available online do not. It is not a 30 minute tutorial that teaches you how to "Train your own neural network" or "Learn deep learning in under 30 minutes". It's a full pipeline which you would need to do if you actually work with machine learning - introducing you to all the parts, and all the implementation decisions and details that need to be made. The dataset is not one of the standard sets like MNIST or CIFAR, you... -
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