Search Results for "neural network visualiser"
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Showing 27 open source projects for "neural network visualiser"
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dibnn
Drop In the Bucket Neural Networks
One more lightweight neural network in C. -
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Darknet YOLO
Real-Time Object Detection for Windows and Linux
This is YOLO-v3 and v2 for Windows and Linux. YOLO (You only look once) is a state-of-the-art, real-time object detection system of Darknet, an open source neural network framework in C. YOLO is extremely fast and accurate. It uses a single neural network to divide a full image into regions, and then predicts bounding boxes and probabilities for each region. This project is a fork of the original Darknet project. -
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NeuralCoref
Fast Coreference Resolution in spaCy with Neural Networks
NeuralCoref is a pipeline extension for spaCy 2.1+ which annotates and resolves coreference clusters using a neural network. NeuralCoref is production-ready, integrated in spaCy's NLP pipeline and extensible to new training datasets. For a brief introduction to coreference resolution and NeuralCoref, please refer to our blog post. NeuralCoref is written in Python/Cython and comes with a pre-trained statistical model for English only. NeuralCoref is accompanied by a visualization client NeuralCoref-Viz, a web interface powered by a REST server that can be tried online. -
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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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Grenade
Deep Learning in Haskell
Grenade is a composable, dependently typed, practical, and fast recurrent neural network library for concise and precise specifications of complex networks in Haskell. Because the types are so rich, there's no specific term level code required to construct this network; although it is of course possible and easy to construct and deconstruct the networks and layers explicitly oneself. Networks in Grenade can be thought of as a heterogeneous list of layers, where their type includes not only the layers of the network but also the shapes of data that are passed between the layers. ... -
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LightSpeedANN
Generator for optimized, vectorized neural net code
This Ruby program takes in a topology specification for an artificial neural network and emits optimized C code (using SSE intrinsics) that implements fast forward and backward propagation for that specific topology. -
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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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NeuroSolutions: Formula Generator
Utility converts the weights file of a MLP Breadboard into a formula
The NeuroSolutions: Formula Generator utility converts the weights file of a default MLP breadboard (1-hidden layer with a TanhAxon in the hidden layer and either a TanhAxon or BiasAxon in the output layer) into a usable formula that can be copied and pasted into your own programs to compute the output of the trained neural network. -
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Fast Artificial Neural Network Library is a free open source neural network library, which implements multilayer artificial neural networks in C with support for both fully connected and sparsely connected networks. Cross-platform execution in both fixed and floating point are supported. It includes a framework for easy handling of training data sets.
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cCNN
A fast implementation of LeCun's convolutional neural network
Code of this library is partialy based on myCNN MATLAB class written by Nikolay Chemurin. -
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pgfann
Neural network library fann for serverside PostgreSQL context
this server side PostgreeSQL extension enables developer to create, store, train and apply artificial neural networks in the serverside context of PostgreSQL. It is based on fann, the Fast Artificial Neural Network Library by Steffen Nissen (Thanks!). -
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It's an object-oriented library written in C++ for creating arbitrary kind of neural networks. The user can use the classes provided to create neural network with arbitrary topology and mixed type of neurons. It's very easy add custom neurons.
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SPLITNEURON is a completely new approach to large-scale, biologically plausible, neural-network simulation library based on data structures and methods directly coded into database and conceived to explicitly share load on multiple machines.
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Yann is Yet Another Neural Network. Yann is a library to create fast neural networks. It is also a GUI to easily create, edit, train, execute and investigate networks. Multiple topologies, runtime properties and ensemble learning are supported.
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This project is the conclusion of three years of research and development of a final project of Bachelor of Engineering Faculty of Univertité Sherbrooke. It consists of a circuit to representing the hand movement with neural network for recognition.
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Speedy Composer
Speedy Composer – Artificial Neural Network Melody Composer.
Thank you for your interest in Speedy Composer. Speedy Composer is an automated application for composing melodies for Speedy Net members. We recently made changes to the source code of Speedy Net, and converted it into the Python language and Django framework. Since Speedy Composer was originally written in PHP, it is not adapted to work with Speedy Net in its current form. So unfortunately we were forced to temporarily close the app Speedy Composer. But don't worry, we kept backups of all... -
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Geeks Artificial Neural Network (G.A.N.N) is an open source project that started with the philosophy of being a new more advanced A.N.N that works as a platform for other applications. In other words, G.A.N.N should be considered as a "Black Box".
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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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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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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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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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The genesis-ai project is an attempt to develop complex neural network behavior by means of evolutionary techniques.
