Introducing Bright Wire

A while ago I created an open-source machine learning library in c#.

This was before TensorFlow and PyTorch became the unstoppable forces that they are today.

It was interesting to learn about how neural networks actually work, and it was (and remains) a challenging design problem - how to balance flexibility with performance in a medium sized library.

One of the design goals was to be able to run machine learning purely in .NET, and I used Bright Wire as the machine learning basis of a bespoke .NET natural language parsing pipeline.

What is Machine Learning?

Machine learning is a type of artificial intelligence (AI) that allows computers to learn without being explicitly programmed.

This is done by training the computer on a dataset of data and examples.

The computer will then use this data to learn how to make predictions or decisions.

There are many different types of machine learning algorithms, each with its own strengths and weaknesses.

Some common algorithms include linear regression, logistic regression, decision trees, and of course neural networks.

Why is Machine Learning Important?

Machine learning is important because it allows computers to solve problems that would be difficult or impossible to solve with traditional programming methods. For example, machine learning can be used to:

• Classify images or text

• Predict future events

• Make recommendations

• Control robots

• Analyze data

Bright Wire Features

• Neural networks

• Feed Forward, Convolutional and Bidirectional 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

• Hierachical Clustering

• Non Negative Matrix Factorisation

• Random Projection

• Tree Based

• Decision Trees

• Random Forest

• K Nearest Neighbour classification