Search Results for "multi-layer perceptron"

This is a small, self-contained framework for training and querying neural networks. Most notably, it contains a lightning-fast "fully fused" multi-layer perceptron (technical paper), a versatile multiresolution hash encoding (technical paper), as well as support for various other input encodings, losses, and optimizers. We provide a sample application where an image function (x,y) -> (R,G,B) is learned. The fully fused MLP component of this framework requires a very large amount of shared memory in its default configuration. ...

This projects aims at creating a simulator for the NARX (Nonlinear AutoRegressive with eXogenous inputs ) architecture with neural networks. The system can fallback to MLP ( multi layer perceptron ), TDNN ( time delay neural network ), BPTT ( backpropagation through time ) and a full NARX architecture. The system is intended to be used as a time series forecaster for educational purposes. This projects is my personal master thesis developed at the Master of Artificial Intelligence at Universitat Polytecnica de Catalunya, Barcelona The project presents artificial generated data but also real data tests from temperature, weather, economy. ...

nunn is a collection of ML algorithms and related examples written in modern C++17.

BNNS is a research tool for interactive training of artificial neural networks based on the Response Function Plots visualization method. It enables users to simulate, visualize and interact in the learning process of a Multi-Layer Perceptron on tasks which have a 2D character. Tasks like the famous two-spirals task or classification of satellite image data.

This project was derived from the following project on CCodeChamp by MR CODER. http://www.ccodechamp.com/c-program-of-multilayer-perceptron-net-using-backpropagation/ I rewrote the project in C++ and and made it more object oriented. Then, I added the capability to use dropout on the hidden layers as specified by Geoffrey Hinton et. al. in "Improving neural networks by preventing co-adaptation of feature detectors" (2012). http://www.cs.toronto.edu/~hinton/absps/dropout.pdf I found...

A C++ program for simulating Kohonen and Multi Layer Perceptron neural network models.

Multi-Core optimized Perceptron Network is a high-performance artificial neural network specially designed for workstations with multi-core CPUs, implemented as a shared library and coded in C++.

Neural Network class library: Its a C/C++ implementation that provides following three neural architecture - Feed-forward network, Radial Basis function network, multi-layer perceptron and Self-Organizing Maps.

MLP-Simulator is first of its kind Multi Layer Perceptron(MLP) Simulator developed using object-oriented methodology. This simulator can be embedded in any Intelligent System, that requires MLP for decision making, using standard API.

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