README.md

Bright Wire is an extensible machine learning library for .NET with optional MKL and GPU support (via CUDA).

Getting Started

Bright Wire is a .net 8 class library.

The previous .net 4.6 version can be found here: https://github.com/jdermody/brightwire-v2

Bright Wire runs "out of the box" with its own vectorised linear algebra library.

If you have a NVIDIA GPU then you can also use GPU based computation. You will need to install NVIDIA CUDA Toolkit 12 (and have a Kepler or better NVIDIA GPU).

To enable higher performance CPU based computation on Intel hardware, Bright Wire also supports the Intel Math Kernel Library (MKL).

Tutorials

• Getting Started
• Introduction
• Classification Overview
• Building a Simple Language Model
• Recognising Handwritten Digits (MNIST)
• Sentiment Analysis
• Text Clustering
• Simple Recurrent Neural Networks
• GRU Recurrent Neural Networks
• Sequence to Sequence Neural Networks with LSTM
• Convolutional Neural Networks

Nuget Installation

To install the cpu version (no CUDA support) use:

Install-Package BrightWire

MKL

To add MKL support use:

Install-Package BrightWire
Install-Package BrightData.MKL

then install the MKL.NET nuget installation for your OS, for example Install-Package MKL.NET.win-x64

CUDA

To add CUDA support use:

Install-Package BrightWire
Install-Package BrightData.Cuda

Features

Neural Networks

• Feed Forward, Convolutional, Bidirectional and Sequence to Sequence (seq2seq) network architectures
• LSTM, GRU, Simple, Elman and Jordan recurrent neural networks
• L2, Dropout and DropConnect regularisation
• Relu, LeakyRelu, Sigmoid, Tanh and SoftMax activation functions
• Gaussian, Xavier and Identity weight initialisation
• Cross Entropy, Quadratic and Binary cost functions
• Momentum, NesterovMomentum, Adagrad, RMSprop and Adam gradient descent optimisations

Bayesian

• Naive Bayes
• Multinomial Bayes
• Multivariate Bernoulli
• Markov Models

Unsupervised

• K Means clustering
• Hierarchical clustering
• Non Negative Matrix Factorisation
• Random Projection

Tree Based

• Decision Trees
• Random Forest

Ensemble Methods

• Stacking

Other

• K Nearest Neighbour classification
• In-memory and file based data processing