Support vector machines (SVMs) and related kernel-based learning algorithms are a well-known class of machine learning algorithms, for non-parametric classification and regression. liquidSVM is an implementation of SVMs whose key features are: fully integrated hyper-parameter selection, extreme speed on both small and large data sets, full flexibility for experts, and inclusion of a variety of different learning scenarios: multi-class classification, ROC, and Neyman-Pearson learning, and least-squares, quantile, and expectile regression.
created at April 22, 2017, 10:16 p.m.
The machine learning toolkit for time series analysis in Python
created at May 4, 2017, 1:08 p.m.
MLBox is a powerful Automated Machine Learning python library.
created at June 1, 2017, 4:59 p.m.
A highly efficient implementation of Gaussian Processes in PyTorch
created at June 9, 2017, 2:48 p.m.
tensorboard for pytorch (and chainer, mxnet, numpy, ...)
created at June 13, 2017, 1:54 p.m.
Multiple Pairwise Comparisons (Post Hoc) Tests in Python
created at June 22, 2017, 7:41 p.m.
A research toolkit for particle swarm optimization in Python
created at July 12, 2017, 12:04 p.m.
A scikit-learn compatible neural network library that wraps PyTorch
created at July 18, 2017, 12:13 a.m.