#SVG

NN-SVG is a tool for creating neural network architecture diagrams parametrically. It facilitates efficient SVG export and supports various neural network types, using D3 and Three.js libraries. Customizable for color, size, and layout, it aids researchers in saving time and serves as a learning resource.

RoughSwift enables the creation of sketchy and hand-drawn style shapes in SwiftUI, optimized for iOS and tvOS. The library provides users with an array of intuitive APIs to craft shapes like lines, rectangles, circles, and SVG paths. It offers diverse customization options, including fill styles and stroke details, to suit various artistic needs. With easy-to-use draw functions and platform-independent capabilities, RoughSwift supports creative transformations and animations. Its robust test coverage and type-safe, immutable structures ensure reliable performance. RoughSwift's multiple fill styles, such as hachure and zigzag, along with SVG scaling, empower developers to create engaging visual content.

React Native SVG Transformer enables easy SVG file integration into React Native projects for both web and mobile platforms. The tool supports Android and iOS by transforming SVGs into compatible React components. It provides step-by-step configuration for React Native and Expo versions, including TypeScript and Jest compatibility, enhancing developer utility. Customizable SVGR options offer flexible SVG handling.

Explore an organized collection of Microsoft architectural icons in PowerPoint that simplifies the integration process for users. This project gathers key SVG icon sets from Microsoft Learn, covering platforms such as Azure, Dynamics365, Power Platform, and Entra, reducing manual effort and ensuring compliance with Microsoft's usage guidelines. Ideal for architectural presentations, the collection is readily available for download, facilitating efficient usage for both designers and developers.

Discover DeepSVG, designed for vector graphics animation using hierarchical deep learning techniques. The project features a training library, the SVG-Icons8 dataset, interactive GUI, and resources for practical learning. Perfect for AI and graphics research, compatible with Python 3.7, and supported by comprehensive documentation and regular updates.