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Fast Artificial Neural Network Library (FANN)

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Networks, Crowds, and Markets: A Book by David Easley and Jon Kleinberg In recent years there has been a growing public fascination with the complex "connectedness" of modern society. This connectedness is found in many incarnations: in the rapid growth of the Internet and the Web, in the ease with which global communication now takes place, and in the ability of news and information as well as epidemics and financial crises to spread around the world with surprising speed and intensity. These are phenomena that involve networks, incentives, and the aggregate behavior of groups of people; they are based on the links that connect us and the ways in which each of our decisions can have subtle consequences for the outcomes of everyone else. Networks, Crowds, and Markets combines different scientific perspectives in its approach to understanding networks and behavior. The book is based on an inter-disciplinary course that we teach at Cornell. You can download a complete pre-publication draft of Networks, Crowds, and Markets here.

OpenNN - Open Neural Networks Library Framework AForge.NET is an open source C# framework designed for developers and researchers in the fields of Computer Vision and Artificial Intelligence - image processing, neural networks, genetic algorithms, fuzzy logic, machine learning, robotics, etc. The framework is comprised by the set of libraries and sample applications, which demonstrate their features: AForge.Imaging - library with image processing routines and filters;AForge.Vision - computer vision library;AForge.Video - set of libraries for video processing;AForge.Neuro - neural networks computation library;AForge.Genetic - evolution programming library;AForge.Fuzzy - fuzzy computations library;AForge.Robotics - library providing support of some robotics kits;AForge.MachineLearning - machine learning library;etc. The work on the framework's improvement is in constants progress, what means that new feature and namespaces are coming constantly.

lucashnegri / NeuralView Bitbucket is a code hosting site with unlimited public and private repositories. We're also free for small teams! Sign up for freeClose NeuralView is a graphical interface for FANN 1, making possible to graphically design, train, and test artificial neural networks. neuralview [OProj - Open Source Software] Bitbucket is a code hosting site with unlimited public and private repositories. We're also free for small teams! Sign up for freeClose NeuralView is a graphical interface for FANN 1, making possible to graphically design, train, and test artificial neural networks.

Fast Artificial Neural Network Library GRATF GRATF stands for Glyph Recognition And Tracking Framework. The project is aimed to provide a library which does localization, recognition and pose estimation of optical glyphs in still images and video files. The library can be used in robotics applications for example, where glyphs may serve as commands or directions to robots. However, most popular application of optical glyph recognition is augmented reality. Here are few demos which were made using the GRATF project. For detailed description of algorithms implemented in this project, you are welcome to read next series of articles: The project includes: Glyph recognition and pose estimation library, which is an extension to AForge.NET framework. In the case you have found any bugs/issues, please, feel free to register them in the issues tracking system.

PyBrain Learning and neural networks Artificial Intelligence: History of AI | Intelligent Agents | Search techniques | Constraint Satisfaction | Knowledge Representation and Reasoning | Logical Inference | Reasoning under Uncertainty | Decision Making | Learning and Neural Networks | Bots An Overview of Neural Networks[edit] The Perceptron and Backpropagation Neural Network Learning[edit] Single Layer Perceptrons[edit] A Perceptron is a type of Feedforward neural network which is commonly used in Artificial Intelligence for a wide range of classification and prediction problems. We can classify people in this problem using a single layer perceptron A perceptron learns by a trial and error like method. To summarize[edit] The neural network starts out kind of dumb, but we can tell how wrong it is and based on how far off its answers are, we adjust the weights a little to make it more correct the next time. . Note: The difference between and is that is what you want the network to produce while is what it actually outputs. and

Java Neural Network Framework Neuroph

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