MLearn

Description

This template library is a personal project to refresh and deepen my knowledge of Machine Learning theory and algorithms.

Algorithms implemented

• Machine Learning
  1. Gaussian processes
  2. Neural networks (Fully connected Networks)
  3. K-means (untested)
• Optimization (untested)
  1. SGD and variants (untested)
  2. BFGS (untested)

Dependencies

• c++11 ( g++-4.9.2 )
• Eigen == 3.3.3
• Boost == 1.64.0 (headers-only)

Demos

• Demo
  • A bash script is provided to setup eveything necessary to run the demos.

Snapshots from experiments

• Gaussian Process regression. Image from this demo
• Filters learned by a Bernoulli-Bernoulli RBM on the MNIST dataset (19 epochs)
• Classification on MNIST with a 3-layer NN. Image from this demo

Disclaimer

When I started this library it was mostly to have some fun implementing deep learning methods and C++ templates. One consequence is that I haven't spent much time testing those algorithms, but they seem to be working. In the future, I will slowly redesign those parts and add the relevant tests.