About: A thin Python wrapper that uses the javabridge Python library to communicate with a Java Virtual Machine executing Weka API calls. Changes:

About: A library of scalable Bayesian generalised linear models with fancy features Changes:

About: This project is a C++ toolkit containing machine learning algorithms and tools for creating complex software in C++ to solve real world problems. Changes:This release adds a number of new features, most important of which is a deep convolutional neural network version of the maxmargin object detection algorithm. This tool makes it very easy to create high quality object detectors. See http://dlib.net/dnn_mmod_ex.cpp.html for an introduction.

About: Somoclu is a massively parallel implementation of selforganizing maps. It relies on OpenMP for multicore execution, MPI for distributing the workload, and it can be accelerated by CUDA on a GPU cluster. A sparse kernel is also included, which is useful for training maps on vector spaces generated in text mining processes. Apart from a command line interface, Python, Julia, R, and MATLAB are supported. Changes:

About: RLScore  regularized leastsquares machine learning algorithms package Changes:Initial Announcement on mloss.org.

About: The scikitlearn project is a machine learning library in Python. Changes:Update for 0.17.1

About: A native Python, scikitcompatible, implementation of a variety of multilabel classification algorithms. Changes:Initial Announcement on mloss.org.

About: ADENINE (A Data ExploratioN pIpeliNE) is a machine learning framework for data exploration that encompasses stateoftheart techniques for missing values imputing, data preprocessing, dimensionality reduction and clustering tasks. Changes:Initial Announcement on mloss.org.

About: TBEEF, a doubly ensemble framework for recommendation and prediction problems. Changes:Included the final technical report.

About: Python toolbox for manifold optimization with support for automatic differentiation Changes:Initial Announcement on mloss.org.

About: A Python library that allows you to define, optimize, and evaluate mathematical expressions involving multidimensional arrays efficiently. Dynamically generates CPU and GPU modules for good performance. Deep Learning Tutorials illustrate deep learning with Theano. Changes:Theano 0.8.1 (29th of March, 2016)
Theano 0.8 (21th of March, 2016)We recommend to everyone to upgrade to this version. Highlights:

About: A Python based library for running experiments with Deep Learning and Ensembles on GPUs. Changes:Initial Announcement on mloss.org.

About: An extensible C++ library of Hierarchical Bayesian clustering algorithms, such as Bayesian Gaussian mixture models, variational Dirichlet processes, Gaussian latent Dirichlet allocation and more. Changes:New maximum cluster argument for all algorithms. Also no more matlab interface since it seemed no one was using it, and I cannot support it any longer.

About: BayesOpt is an efficient, C++ implementation of the Bayesian optimization methodology for nonlinearoptimization, experimental design and stochastic bandits. In the literature it is also called Sequential Kriging Optimization (SKO) or Efficient Global Optimization (EGO). There are also interfaces for C, Matlab/Octave and Python. Changes:Fixed bug in save/restore. Fixed bug in initial design.

About: Easily prototype WEKA classifiers and filters using Python scripts. Changes:0.3.0
0.2.1
0.2.0
0.1.1
0.1.0

About: An opensource Python toolbox to analyze mobile phone metadata. Changes:Initial Announcement on mloss.org.

About: This algorithm is described in Deep Semantic Ranking Based Hashing for MultiLabel Image Retrieval. See https://github.com/zhaofang0627/cudaconvnetforhashing Changes:Initial Announcement on mloss.org.

About: Efficient and Flexible Distributed/Mobile Deep Learning Framework, for python, R, Julia and more Changes:This version comes with Distributed and Mobile Examples

About: Variational Bayesian inference tools for Python Changes:

About: Optunity is a library containing various optimizers for hyperparameter tuning. Hyperparameter tuning is a recurrent problem in many machine learning tasks, both supervised and unsupervised.This package provides several distinct approaches to solve such problems including some helpful facilities such as crossvalidation and a plethora of score functions. Changes:This minor release has the same feature set as Optunity 1.1.0, but incorporates several bug fixes, mostly related to the specification of structured search spaces.
