Search Results for "python neural network" - Page 17
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Showing 440 open source projects for "python neural network"
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HOPEN (Hopfield Open Network) is a program for patterns recognition based on a Hopfield Artificial Neural Network. It can learn using Hebb's Rule, Iterative Learning Scheme and Repeated Hebbian Learning.
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Amygdala is a C++ spiking neural network library. It includes several neuron models, SMP support and facilities for developing SNNs with genetic algorithms. Support for running Amygdala neural networks on workstation clusters and MPPs is also under way
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Lightweight backpropagation neural network in C. Intended for programs that need a simple neural network and do not want needlessly complex neural network libraries. Includes example application that trains a network to recognize handwritten digits.
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This library provides a network abstraction layer for heterogenous large scale modular systems. It supports clearly specified interface definitions for communication between modules, while maximizing module indendence and adaptability.
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A neural net module written in python. The aim of the project is to provide a large set of neural network types accessed by an API that is easy to use and powerful.
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Neural network libraries in many different languages compatible with each other, such that neural networks can be trained on one platform and utilised on another.
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Do you want a neural network OO class? With documentation? Do not go further... You found it! you have here a complete library of different neural network in an OO encapsulation. Starting from adaline, back propagation, Kohonen
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NeuralNetworkToolkit is an object oriented neural network API written in Java for modelling, implementing and training artificial neural networks.
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TIKAPP is becoming a collection of tools for simulation of neural networks. The first available part is an ANSI-C++ library with support for backpropation networks.
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Multilayered feed-forward neural network software written in C++. Backpropagation and RPROP are available as training algorithms. Design goals: speed of execution when calculating the output to new data, and quality of training (preprocessing: PCA).
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nn-utility is a neural network library for C++ and Java. Its aim is to simplify the tedious programming of neural networks, while allowing programmers to have maximum flexibility in terms of defining functions and network topology.
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Mental is a neural network engine implemented in pure Python. Design goals are easy-to-read source code, completeness, and ease of use. Presently implements feed-forward, back-propagation networks. Intended primarily for instructional use.
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Cluster Networks are a new style of neural simulation / neural network modeling, that models networks of neural populations ("clusters") that transform and transmit information using precisely-timed, graded bursts ("pulses" or "volleys") of firing.
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Yawn: Yet Another W* Network: a java framework for developing, training and testing neural networks independently of the model and test environment.
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Net.py is a tool for learning about neural nets. Currently, it only allows the user to experiment with a Hopfield net. I am now extending it to cover the Kohonen net. I'd be pleased to receive suggestions and criticism.
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EasyAI is a project to build classical AI program and method in Java language, such as resolution for logic, greedy search, genetic algorithm, neural network and so on...
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Neural network library for C++ applications in Windows and Linux. Multi-Layer perceptron, radial-basis function networks and Hopfield networks are supported. You can interface this with Matlab's Neural Network Toolbox using the Matlab Extensions Pack
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Simbrain is a java-based neural network. It is no longer being hosted here. Most information and downloads are at the homepage, www.simbrain.net. The development page is http://code.google.com/p/simbrain/
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GNNS - GNNS Neural Network Simulator, is both a set of libraries and an interface for creating and learning neural networks. It is aimed to support as many network typs and learning algorithms as possible. GNNS is meant to support different platforms.
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Zeus is a small, lightweight, very fast object oriented framework for developing neural networks with an integrated shell interface.
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AsyncNN is a implementation of a neural network that allows individual neurons to run asyncronously. The network allows dynamic creation of new links AND neurons. This software will eventually provide a platform for exploring live training. Need feedback
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RISO: distributed, heterogeneous Bayesian belief networks. Belief network: a probability model defined on an acyclic directed graph; distributed: nodes can be on different hosts; and heterogeneous: allowing different types of conditional distributions.
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A java based neural network framework. The Auratus network is built around an XML messaging system, allowing for a complete MVC design. Additionally, Auratus networks are constructed and at the node/edge level, allowing for advanced topologies.
