A feedforward neural network with resilient backpropagation (Rprop). It's ~250 loc, 100% Ruby, with no external dependencies.

This implementation trains significantly faster than ai4r's backpropagation neural network, mainly because the Rprop training algorithm implemented here is much faster than the non-batch backpropagation algorithm used in ai4r.

However, this implementation is significantly slower than ruby-fann, which wraps the FANN library, written in C. If you're looking for something production-ready, check out ruby-fann.

• iris.rb: solves a simple classification problem: predict the species of iris flower based on sepal and petal size.
• mpg.rb: solves a simple regression problem: predict the miles per gallon of a car based on car attributes.
• mnist.rb: performs OCR on handwritten digits. Requires download of MNIST dataset; see instructions at top of file.

• Introduction to the Math of Neural Networks
• Neural Networks and Deep Learning
• Thoughtful Machine Learning: A Test-Driven Approach
• Hacker's guide to Neural Networks
• https://github.com/harthur/brain
• The RPROP Algorithm
• Resources for Machine Learning in Ruby

MIT