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https://github.com/jdermody/brightwire
Bright Wire is an open source machine learning library for .NET with GPU support (via CUDA)
https://github.com/jdermody/brightwire
convolutional-neural-networks csharp cuda cuda-support gpu gpu-support machine-learning machine-learning-library machinelearning neural-network recurrent-neural-networks
Last synced: about 2 months ago
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Bright Wire is an open source machine learning library for .NET with GPU support (via CUDA)
- Host: GitHub
- URL: https://github.com/jdermody/brightwire
- Owner: jdermody
- License: mit
- Created: 2016-05-17T03:06:21.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2024-05-19T22:30:47.000Z (4 months ago)
- Last Synced: 2024-05-28T14:46:37.765Z (4 months ago)
- Topics: convolutional-neural-networks, csharp, cuda, cuda-support, gpu, gpu-support, machine-learning, machine-learning-library, machinelearning, neural-network, recurrent-neural-networks
- Language: C#
- Homepage: https://github.com/jdermody/brightwire/wiki
- Size: 46.6 MB
- Stars: 119
- Watchers: 24
- Forks: 18
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-dotnet-machine-learning - Bright Wire
README
*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](https://developer.nvidia.com/cuda-downloads)
(and have a [Kepler or better NVIDIA GPU](https://en.wikipedia.org/wiki/CUDA#GPUs_supported)).
To enable higher performance CPU based computation on Intel hardware, *Bright Wire* also supports the Intel Math Kernel Library (MKL).
## Tutorials
* [Getting Started](https://github.com/jdermody/brightwire/wiki/0.-Getting-Started)
* [Introduction](https://github.com/jdermody/brightwire/wiki/01.-Introduction)
* [Classification Overview](https://github.com/jdermody/brightwire/wiki/02.-Classification-Overview)
* [Building a Simple Language Model](https://github.com/jdermody/brightwire/wiki/03.-Generating-Text-with-Markov-Chains)
* [Recognising Handwritten Digits (MNIST)](https://github.com/jdermody/brightwire/wiki/04.-Recognising-Handwritten-Digits-(MNIST))
* [Sentiment Analysis](https://github.com/jdermody/brightwire/wiki/05.-Sentiment-Analysis)
* [Text Clustering](https://github.com/jdermody/brightwire/wiki/06.-Text-Clustering-Four-Ways)
* [Simple Recurrent Neural Networks](https://github.com/jdermody/brightwire/wiki/07.-Teaching-a-Recurrent-Neural-Net-Binary-Addition)
* [GRU Recurrent Neural Networks](https://github.com/jdermody/brightwire/wiki/08.-GRU-Recurrent-Neural-Networks)
* [Sequence to Sequence Neural Networks with LSTM](https://github.com/jdermody/brightwire/wiki/09.-Sequence-to-Sequence-with-LSTM)
* [Convolutional Neural Networks](https://github.com/jdermody/brightwire/wiki/10.-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