Search Results for "artificial intelligence algorithm" - Page 3
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Showing 258 open source projects for "artificial intelligence algorithm"
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DaNNet
Deep Artificial Neural Network framework using Armadillo
DaNNet is a C++ deep neural network library using the Armadillo library as a base. It is intended to be a small and easy to use framework with no other dependencies than Armadillo. It uses independent layer-wise optimization giving you full flexibility to train your network. -
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Simd
High performance image processing library in C++
The Simd Library is a free open source image processing library, designed for C and C++ programmers. It provides many useful high performance algorithms for image processing such as: pixel format conversion, image scaling and filtration, extraction of statistic information from images, motion detection, object detection (HAAR and LBP classifier cascades) and classification, neural network. The algorithms are optimized with using of different SIMD CPU extensions. In particular the library... -
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Caffe Framework
Caffe, a fast open framework for deep learning
Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR) and by community contributors. Yangqing Jia created the project during his PhD at UC Berkeley. Caffe is released under the BSD 2-Clause license. Expressive architecture encourages application and innovation. Models and optimization are defined by configuration without hard-coding. Switch between CPU and GPU by setting a single flag to train on a GPU machine... -
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VideoMan Library
C++ library for image acquisition and visualization
Library for capturing video from cameras, 3d sensors, frame-grabbers, video files and image sequences. It can also display multiple images using OpenGL with different layouts. Easy integration with OpenCV, CUDA... Perfect for computer vision. Keywords: video capture, computer vision, machine vision, opencv, opengl, cameras, video input devices, firewire, usb, gige -
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Madara
Middleware for distributed applications
The purpose of the project is to develop a portable programming framework that facilitates distributed and multi-threaded programming for C++, Java, and Python. MADARA was originally developed as an agent-based middleware specifically for real-time, distributed artificial intelligence, but is now more general purpose for distributed timing, control, knowledge and reasoning, and quality-of-service. MADARA is composed of several tools and middleware, and the main entry point into the system... -
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NOMAD is a C++ code that implements the MADS algorithm (Mesh Adaptive Direct Search) for difficult blackbox optimization problems. Such problems occur when the functions to optimize are costly computer simulations with no derivatives.
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The Edge Machine Learning library
Machine learning algorithms for edge devices
Machine learning models for edge devices need to have a small footprint in terms of storage, prediction latency, and energy. One instance of where such models are desirable is resource-scarce devices and sensors in the Internet of Things (IoT) setting. Making real-time predictions locally on IoT devices without connecting to the cloud requires models that fit in a few kilobytes.These algorithms can train models for classical supervised learning problems with memory requirements that are... -
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C++ Fuzzy Logic Programming Library - For problem solving in the fuzzy logic domain. It has an object oriented API well suited to C++ syntax for direct inclusion in source code. Portable to platforms that support the C++ STL.
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Command Line Parser GetPot
Tool to parse the command line and configuration files.
Powerful command line and configuration file parsing for C++, Python, Ruby and Java (others to come). This tool provides many features, such as separate treatment for options, variables, and flags, unrecognized object detection, prefixes and much more. -
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Caffe
A fast open framework for deep learning
Caffe is an open source deep learning framework that’s focused on expression, speed and modularity. It’s got an expressive architecture that encourages application and innovation, and extensible code that’s great for active development. Caffe also offers great speed, capable of processing over 60M images per day with a single NVIDIA K40 GPU. It’s arguably one of the fastest convnet implementations around. Caffe is developed by the Berkeley AI Research (BAIR)/The Berkeley Vision and... -
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Library to help the creation of the dynamic systems, like simulators or games. Key word : 3D Rendering, Multi-Agent system, Collision detection, Game
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BWEM
Fast and robust map analyser for Brood War.
Brood War Easy Map is a C++ library that analyses Brood War's maps and provides relevant information such as areas, choke points and base locations. It is built on top of the BWAPI library. It first aims at simplifying the development of bots for Brood War, but can be used for any task requiring high level map information. It can be used as a replacement for the BWTA2 add-on, as it performs faster and shows better robustness while providing similar information. -
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OpenNLC
Natural Language Compiler
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OpenANN
Basic Artificial Neural Network
OpenANN is a basic artificial neural network toolset. It is not being actively maintained. For performance neural networking, consider an alternative (such as https://sourceforge.net/projects/openann-project or https://www.tensorflow.org) -
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OpenATOR
Axis Transformation Object Recognition
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node-opencv
OpenCV Bindings for node.js
OpenCV bindings for Node.js. OpenCV is the defacto computer vision library - by interfacing with it natively in node, we get powerful real time vision in js. People are using node-opencv to fly control quadrocoptors, detect faces from webcam images and annotate video streams. If you're using it for something cool, I'd love to hear about it! You'll need OpenCV 2.3.1 or newer installed before installing node-opencv. You can use opencv to read in image files. Supported formats are in the OpenCV... -
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OSAR Appliances
The OSAR Linux Image for Augmented Reality and Computer Vision
The OpenSUSE Augmented Reality (OSAR) distribution is the first GNU / Linux appliance created for the exclusive development of applications with augmented reality technology and games with computer vision. -
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nunn
This is an implementation of a machine learning library in C++17
nunn is a collection of ML algorithms and related examples written in modern C++17. -
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Kohonen neural network library is a set of classes and functions for design, train and use Kohonen network (self organizing map) which is one of AI algorithms and useful tool for data mining and discovery knowledge in data (http://knnl.sf.net).
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reacTIVision is a computer vision framework for the fast and robust tracking of markers attached on physical objects, and the creation of multi-touch surfaces. It was designed for the rapid development of table-based tangible user interfaces.
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Distant Speech Recognition
Beamforming and Speech Recognition Toolkit
BTK contains C++ and Python libraries that implement speech processing and microphone array techniques such as speech feature extraction, speech enhancement, speaker tracking, beamforming, dereverberation and echo cancellation algorithms. The Millennium ASR provides C++ and python libraries for automatic speech recognition. The Millennium ASR implements a weighted finite state transducer (WFST) decoder, training and adaptation methods. These toolkits are meant for facilitating research and... -
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MicroGP
A multi-purpose extensible self-adaptive evolutionary algorithm
MicroGP (µGP, ugp) is a versatile optimizer able to outperform both human experts and conventional heuristics in finding the optimal solution of hard problems. It is an evolutionary algorithm since it mimics some principles of the Neo-Darwinian paradigm. ⚠️ A new version is available on https://github.com/squillero/microgp4 -
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Behaviour Control Framework
A C++ framework that uses inhibition trees for behaviour control.
...! The structure and implementation of this library focuses on the application of the inhibition-based behaviour control architecture to real-time control loops, but can reasonably be adapted for a wide variety of other artificial intelligence applications. Emphasis has been placed on having very low overhead so as not to hurt overall system performance, while still maintaining ease of use. I welcome all feedback, suggestions and bug reports! Email: pallgeuer[at]ais.uni-bonn.de
