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Showing 3 open source projects for "reference"
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We help you deliver Virtual and Hybrid Events using our Award Winning end-to-end Event Management PlatformEventsAIR have been anticipating and responding to the ever-changing event industry needs for over 30 years, providing innovative solutions that empower event organizers to create successful events around the globe.
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Infor M3 ERPEfficiently executing the complex processes of enterprise manufacturers and distributors. Infor M3 is a cloud-based, manufacturing and distribution ERP system that leverages the latest technologies to provide an exceptional user experience and powerful analytics in a multicompany, multicountry, and multisite platform. Infor M3 and related CloudSuite™ industry solutions include industry-leading functionality for the chemical, distribution, equipment, fashion, food and beverage, and industrial manufacturing industries. Staying ahead of the competition means staying agile. Our new capabilities bring improved data-driven insights and streamlined workflows to help you make informed decisions and take quick action.
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nvim-config
A modern Neovim configuration with full battery
nvim-config is a personal, well-documented Neovim configuration maintained by jdhao, optimized for productivity and modern plugin ecosystems. It includes setups for language servers, autocompletion, syntax highlighting, fuzzy finding, and aesthetics. This configuration is a great starting point or reference for users setting up a performant and feature-rich Neovim environment. -
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Gideros Mobile
Cross platform SDK
...While Gideros is mainly dedicated to game development, it is also a good fit for professional apps. Code once, run everywhere! Intro documentation: http://docs.giderosmobile.com/ Reference documentation: http://docs.giderosmobile.com/reference/ Forum: http://giderosmobile.com/forum Full source code: https://github.com/gideros/gideros -
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char-rnn
Multi-layer Recurrent Neural Networks (LSTM, GRU, RNN)
char-rnn is a classic codebase for training multi-layer recurrent neural networks on raw text to build character-level language models that learn to predict the next character in a sequence. It supports common recurrent architectures including vanilla RNNs as well as LSTM and GRU variants, letting users compare behavior and output quality across model types. It is straightforward: you provide a single text file, train the model to minimize next-character prediction loss, then sample from the...
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