A Julia package for Gaussian Processes
created at April 30, 2015, 2:46 p.m.
Example code and materials that illustrate using neural networks with several hidden layers in SAS.
created at May 8, 2015, 3:20 p.m.
Example code and materials that illustrate applications of SAS machine learning techniques.
created at May 8, 2015, 3:20 p.m.
Reinforcement learning using Markov Decision Processes. For JS, written in C++.
created at May 10, 2015, 4:52 p.m.
Deep Learning API and Server in C++14 support for PyTorch,TensorRT, Dlib, NCNN, Tensorflow, XGBoost and TSNE
created at May 22, 2015, 2:45 p.m.
A collection of machine learning algorithms for online linear classification written in Common Lisp
created at May 26, 2015, 11:56 a.m.
A library for machine learning that builds predictions using a linear regression.
created at June 8, 2015, 4:52 p.m.
Reinforcement Learning environments based on the 1993 game Doom
created at June 26, 2015, 6:38 p.m.
ZPar statistical parser. Universal language support (depending on the availability of training data), with language-specific features for Chinese and English. Currently support word segmentation, POS tagging, dependency and phrase-structure parsing.
created at June 30, 2015, 1:55 p.m.
Python package for Bayesian Machine Learning with scikit-learn API
created at July 30, 2015, 3:15 a.m.
A lightweight C++ machine learning library for embedded electronics and robotics.
created at Aug. 7, 2015, 2:54 a.m.
Naive Bayesian Classifier written in APL
created at Sept. 13, 2015, 3:27 p.m.