Search Results for "back propagation"
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Showing 34 open source projects for "back propagation"
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Liveblocks
Liveblocks gives you the building blocks and infrastructure
...Rather than building real-time synchronization from scratch, developers can leverage Liveblocks’ SDKs and APIs to focus on their product’s unique logic while relying on robust back-end support for distributed state and event propagation. The platform is designed to work seamlessly with modern frameworks, offering pre-built components and integration guides that reduce complexity and accelerate development of features that would otherwise require substantial real-time engineering. -
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Agents 2.0
An Open-source Framework for Data-centric Language Agents
Agents is an open-source framework designed to build and train autonomous language agents through a data-centric and learning-oriented architecture. The project introduces a concept known as agent symbolic learning, which treats an agent pipeline similarly to a neural network computational graph. In this framework, each node in the pipeline represents a step in the reasoning or action process, while prompts and tools act as adjustable parameters analogous to neural network weights. During... -
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TensorBoardX
tensorboard for pytorch (and chainer, mxnet, numpy, etc.)
The SummaryWriter class provides a high-level API to create an event file in a given directory and add summaries and events to it. The class updates the file contents asynchronously. This allows a training program to call methods to add data to the file directly from the training loop, without slowing down training. TensorboardX now supports logging directly to Comet. Comet is a free cloud based solution that allows you to automatically track, compare and explain your experiments. It adds a... -
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ServiceTalk
A networking framework that evolves with your application
ServiceTalk is a JVM network application framework with APIs tailored to specific protocols (e.g. HTTP/1.x, HTTP/2.x, etc…) and supports multiple programming paradigms. ServiceTalk is intended to provide a common and extensible networking abstraction on top of a lower-level networking framework (e.g. Netty). Netty is a great low-level networking framework, but when used for service-to-service communication it presents a few opportunities for improvement. Fully asynchronous and requires... -
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sijinn
SImple Java Implementation of Neural Network
...Perseptron network structure. Can be saved in xml format. Training strategies: - Gradient Descent (Batch, Stochastic) - Genetic Breeding Training algorithms: - BPROP (back propagation) - QPROP (quick propagation) - RPROP (resilient propagation) - GENE (genetic neural) Demo: https://sijinn.herokuapp.com/ http://sijinn.appspot.com/network GitHub: https://github.com/surban1974/sijinn Maven <repositories> <repository> <id>neohort-mvn-repo</id> <url>https://github.com/surban1974/sijinn/raw/mvn-repo/</url> <snapshots> <enabled>true</enabled> <updatePolicy>always</updatePolicy> </snapshots> </repository> </repositories> <dependency> <groupId>com.github.surban1974.sijinn</groupId> <artifactId>sijinn-base</artifactId> <version>1.1.2-alfa</version> </dependency> -
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AIEditor
Artificial Intelligence Editor in Java
Create, edit, adjust your neural networks. Train them to get espectacular results. -
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GPU Machine Learning Library. This library aims to provide machine learning researchers and practitioners with a high performance library by taking advantage of the GPU enormous computational power. The library is developed in C++ and CUDA.
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Grenade
Deep Learning in Haskell
...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. To perform back propagation, one can call the eponymous function which takes a network, appropriate input, and target data, and returns the back propagated gradients for the network. The shapes of the gradients are appropriate for each layer and may be trivial for layers like Relu which have no learnable parameters. -
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Multiple Back-Propagation (with CUDA)
Open source software for training neural networks
Multiple Back-Propagation is an open source software application for training neural networks with the backpropagation and the multiple back propagation algorithms. Currently this project is also hosted at http://code.google.com/p/multiplebackpropagation -
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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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Particle Correlation Analysis
Simulation program
...Whereby these layers contain particles. The wavefront passing through the predetermined volume is thus scattered by the particles in each layer. Axial hologram is reconstructed by numerical back propagation of the wavefront to the first layer of volume. Further, two adjacent layers with particles are obtained. These layers are subsequently compared using the correlation function. It is highly recommended to use default parameters of the program, only changing N, Np, D, ∆z and the distribution regimes. You can see supporting information in Specification of the program.docx -
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ANNGD
Artificial neural network gender detection
...The detection is made in real time images captured by webcam by opencv library. The package include applications to image pre-processing and artificial neural network back-propagation training. -
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Aquila
Software Architecture for Cognitive Robotics
Aquila 2.0, an open-source cross-platform software architecture for cognitive robotics that makes use of independent heterogeneous CPU-GPU modules with loosely coupled dynamically generated graphical user interfaces. -
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Simple program for artificial neural network users. Right now the program can manipulate with Feed forward back propagation network.
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Cerebellum Granule Neuron model
This is a detailed NEURON Model for a cerebellum granule cell.
...Numerical simulations indicated that granule cells have a compact electrotonic structure allowing EPSPs to diffuse with little attenuation from dendrites to axon. The spike arose almost simultaneously along the whole axonal ascending branch and invaded the hillock, whose activation promoted spike back-propagation with marginal delay (<200 micros) and attenuation (<20 mV) into the somato-dendritic compartment. For details check the cited article. Diwakar S, Magistretti J, Goldfarb M, Naldi G, D`Angelo E (2009) Axonal Na+ channels ensure fast spike activation and back-propagation in cerebellar granule cells J Neurophysiol 101(2):519-32 -
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Parallel Neural Networks
Neural networks in CUDA & OpenCL with back propagation algorithm
This project is my engineering diploma. It's aim is to compare the efficiency of both technologies and to check where which hacks works better. What is more one of my tasks is to compare different ways of decomposing computations in parallel. -
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C-IL2P
Original C-IL2P
This is a C++ implementation of the original C-IL2P system, invented by Artur D'Avila Garcez and Gerson Zaverucha. C-IL2P is a neural-symbolic learning system which uses a propositional logic program to create a three-layer recursive neural network and uses back-propagation to learn from examples. -
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galprop_skymap_convert
Convert galprop skymaps to standard healpix
A routine for converting GALPROP-generated HealPix skymaps to a compatible format which can be plotted by Aladin (v7), available from CDS at http://aladin.u-strasbg.fr. This allows free rotation of the sphere, avoiding the distortions of Aitoff and other projections often used. It is a very practical way to view and prepare skymap plots of GALPROP output. GALPROP outputs a spectrum per HealPix pixel, while Aladin requires each energy to be stored as a column. A conversion is required to... -
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AHANNS
AHANNS - Amateur Homemade Artificial Neural Network Simulator
AHANNS enables the user to create and train artificial neural networks. Training is carried out by an implementation of back propagation learning algorithm. See the documentation for details. This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. ... -
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NOTE: This project was moved to: https://github.com/eetorres The BPANNA is a flexible Back propagation neural network, which include the Conjugate Gradient and the Levenberg-Marquardt. You can change the number of inputs, number of layers, number of neurons per layer and outputs. It included an structure editor.
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Swarm Wars
Safety in numbers.
...They can select mates, and they can gather and distribute food and material. This behavior is controlled by a genetically evolved neural net augmented with online back propagation learning. The back propagation learning uses a reward vector and plasticity matrix that is evolved as part of the genome. Long story short, the AI is pretty frickin' sophisticated. Players can take control of organisms, trade resources and organisms in a market, and aid evolution by selective breeding. -
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Library for creation of artificial neural networks in C#. Library supports Hopfield nets, Kohonen nets, feedforward networks, several learning techniques (differential evolution, back propagation, quick propagation, resilient propagation etc.)
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Face recognition using BPNN. Contains 1.Face recognition using Back propagation neural network (customize code) code using matlab. 2. Face recognition using Back propagation network (builtin) code using matlab.Project closed for now,Adeel Raza Azeemi
