Jack Dermody

Introducing Bright Wire

Bright WireA 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:

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