Search Results for "learning system"
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Showing 23 open source projects for "learning system"
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whisper.cpp
Port of OpenAI's Whisper model in C/C++
whisper.cpp is a lightweight, C/C++ reimplementation of OpenAI’s Whisper automatic speech recognition (ASR) model—designed for efficient, standalone transcription without external dependencies. The entire high-level implementation of the model is contained in whisper.h and whisper.cpp. The rest of the code is part of the ggml machine learning library. The command downloads the base.en model converted to custom ggml format and runs the inference on all .wav samples in the folder samples.... -
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fairseq2
FAIR Sequence Modeling Toolkit 2
fairseq2 is a modern, modular sequence modeling framework developed by Meta AI Research as a complete redesign of the original fairseq library. Built from the ground up for scalability, composability, and research flexibility, fairseq2 supports a broad range of language, speech, and multimodal content generation tasks, including instruction fine-tuning, reinforcement learning from human feedback (RLHF), and large-scale multilingual modeling. Unlike the original fairseq—which evolved into a... -
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SynaptaOS
Synapta OS is a preconfigured educational Linux distribution with AI
🌐 Synapta OS Synapta OS is an educational Linux distribution preconfigured with local Artificial Intelligence (AI) capabilities. It is designed for schools and remote areas where Internet connectivity is limited or unavailable, offering access to AI-based learning and digital tools offline. Version 1.4.6 -
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Amoeba
Linux Command Line Learning Program
Amoeba is a Linux command-line learning program that observes and adapts to the Linux command line storing learned strings and their usage data. It enhances command-line proficiency by capturing command outputs, adapting string lengths, and periodically saving knowledge. Sandboxing is essential for security, and optionally a virtual machine would further isolates it from the host system. -
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Unitag is a language-independent Unicode-based part-of-speech tagging system. Written entirely in ANSI-compatible C, it should (in theory) compile on any OS, but has been tested on 32-bit Windows.
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virgo
32 bit VIRGO Linux Kernel
Linux kernel fork-off with cloud and machine learning features -
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DarkForestGo
DarkForest, the Facebook Go engine
darkforestGo is an early deep-reinforcement-learning Go engine that combined a convolutional policy/value network with Monte Carlo Tree Search (MCTS) to play the full 19×19 game at a strong amateur level. The system couples fast GPU policy inference with CPU or GPU-assisted tree search so priors from the network guide exploration while search refines local tactics. Training pipelines mix supervised learning from human professional games and self-play fine-tuning, allowing the model to learn opening patterns and endgame tactics beyond simple pattern libraries. ... -
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HYBRYD
Library written in C with Python API for IPv6 networking
This project is a rewritten of an initial project that I've called GLUE and created in 2005. I'm trying to readapt it for Python 2.7.3 and GCC 4.6.3 The library has to be build as a simple Python extension using >python setup.py install and allows to create different kind of servers, clients or hybryds (clients-servers) over (TCP/UDP) using the Ipv6 Protocol. The architecture of the code is based on brain architecture. Will put an IPv6 adress active available as soon as... -
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LensOSX
LensOSX: the light, efficient network simulator
Lens is the light, efficient network simulator, written by Doug Rohde. LensOSX is a native MacOSX port of Lens that runs on MacOSX 10.5 or higher, created by Harm Brouwer, Daniel de Kok and Hartmut Fitz. -
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bnns
Research tool for interactive training of artificial neural networks.
BNNS is a research tool for interactive training of artificial neural networks based on the Response Function Plots visualization method. It enables users to simulate, visualize and interact in the learning process of a Multi-Layer Perceptron on tasks which have a 2D character. Tasks like the famous two-spirals task or classification of satellite image data. -
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A base for programs. Includes algorythms for Q-learning and SOM's etc. too. Examples: Hamron: Simulates evolution, uses the 2D-renderer. DriveUnit: created for school, for a robotic arm, uses the 3D-renderer. Hlearn: http://www.sagenb.org/home/pub/8
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MyNook
A machine learning system for supervised document classification
An open source system for supervised document classification based on statistical machine learning techniques. On the contrary of the state of art classification techniques, MyNook just requires the title of the document, not the content itself. -
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GP System in C/C++
GP System in C/C++
This is a very elementary GP system written in C/C++ of symbolic regression,The input to the program is the file containing Terminal set. -
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The intention of this project is to give all serious users of the SNNS a place where they find a bugfix and patch management and where they get useful information about the SNNS.
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MoMS (Model Management System) is a model management system for statistical models, a little bit like a database management system. Instead of having tables, we have models that can be updated and queried.
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Weekend-robotics enables the dedicated amateur to build a autonomous robot. It runs on a Linux system for high-level operations and offers an interface to the defacto standard hardware abstraction layer in robotics, Player/Stage.
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A packet dissector driven by machine learning algorithms. You train it to recognize specific types of packets by showing it examples and counterexamples of some packet type, and it will figure out which bits in the packet define it as the type you seek.
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AISK is an acronym for Artificial Intelligence Spam Killer. It is a server side filter that acts as a kind of PROXY and uses SMTP to communicate with MTA. Main decision mechanism is based on artificial neural network capable of finding patterns, learning
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Artificial vision library. Objectives are to make an OCR, fingerprint and face identification as some applications through a general purpose learning and pattern relationships algorithm (Currently performs very basic identification).
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CompLearn is a general-purpose compression-based machine learning system that uses data compression to learn or data mine patterns in arbitrary data.
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The purpose of the project is to make the bash behave intelligent. This includes currently corrrection of mistyped program names, autocorrection while pressing TAB and learning which program are associated with which suffix.
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