About: A Graphical Tool for Designing and Training Deep Neural Networks

• new Caffe version manager
• added separate executable for remote server
• new Session cloning behavior
• improved Host Manager design
• change database location in Input Manager
• many smaller improvements and bugfixes

About: SMPyBandits: an Open-Source Research Framework for Single and Multi-Players Multi-Arms Bandits (MAB) Algorithms in Python

Initial Announcement on mloss.org.

About: A thin Python3 wrapper that uses the javabridge Python library to communicate with a Java Virtual Machine executing Weka API calls.

• upgraded to Weka 3.9.2
• properly initializing package support now, rather than adding package jars to classpath
• added weka.core.ClassHelper Java class for obtaining classes and static fields, as javabridge only uses the system class loader

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

• upgraded to Weka 3.9.2
• properly initializing package support now, rather than adding package jars to classpath
• added weka.core.ClassHelper Java class for obtaining classes and static fields, as javabridge only uses the system class loader

About: pycobra is a python library for ensemble learning, which serves as a toolkit for regression, classification, and visualisation. It is scikit-learn compatible and fits into the existing scikit-learn ecosystem.

pycobra is further pep8 compliant, has improved tests and more plotting options.

About: A Spark package implementing algorithms for learning from crowdsourced big data.

Changes: - Minor improvements in code and documentation

About: A method to optimize the hyperparameters for machine learning methods implemented in Scikit-learn based on Derivative Free Optimization

Initial Announcement on mloss.org.

About: DynaML is a Scala environment for conducting research and education in Machine Learning. DynaML comes packaged with a powerful library of classes implementing predictive models and a Scala REPL where one can not only build custom models but also play around with data work-flows.

Initial Announcement on mloss.org.

About: OpenNN is a software library written in C++ for advanced analytics. It implements neural networks, the most successful machine learning method. The library has been designed to learn from both data sets and mathematical models.

New algorithms, correction of bugs.

About: A native Python, scikit-compatible, implementation of a variety of multi-label classification algorithms.

• a general matrix-based label space clusterer has been added which can cluster the output space using any scikit-learn compatible clusterer (incl. k-means)
• support for more single-class and multi-class classifiers you can now use problem transformation approaches with your favourite neural networks/deep learning libraries: theano, tensorflow, keras, scikit-neuralnetworks
• support for label powerset based stratified kfold added
• graph-tool clusterer supports weighted graphs again and includes stochastic blockmodel calibration
• bugs were fixed in: classifier chains and hierarchical neuro fuzzy clasifiers

About: NaN-toolbox is a statistics and machine learning toolbox for handling data with and without missing values.

Changes in v.3.1.2 - improve configuration and build system - support of more platforms (including Octave 4.2.0) improved

Changes in v.3.0.3 - improve compatibility for Octave on Windows

Changes in v.3.0.1 - fix packaging for octave

Changes in v.2.8.5 - bug fix: trimmean - compiler support for gcc-5 and clang - fix typos

For details see the CHANGELOG at http://pub.ist.ac.at/~schloegl/matlab/NaN/CHANGELOG

About: General purpose Java Machine Learning library for classification, regression, and clustering.

See github release tab for change info

About: This is an optimization library based on Social Impact Theory(SITO). The optimizer works in the same way as PSO and GA.

bug removed

About: Python toolbox for manifold optimization with support for automatic differentiation

Initial Announcement on mloss.org.

About: Lua-MapReduce framework implemented in Lua using luamongo driver and MongoDB as storage. It follows Iterative MapReduce for training of Machine Learning statistical models.

• Updated to work with luamongo v0.5.0, which uses C++ driver legacy-1.1.0

About: APRIL-ANN toolkit (A Pattern Recognizer In Lua with Artificial Neural Networks). This toolkit incorporates ANN algorithms (as dropout, stacked denoising auto-encoders, convolutional neural networks), with other pattern recognition methods as hidden makov models (HMMs) among others.

• Updated home repository link to follow april-org github organization.
• Improved serialize/deserialize functions, reimplemented all the serialization procedure.
• Added exceptions support to LuaPkg and APRIL-ANN, allowing to capture C++ errors into Lua code.
• Added set class.
• Added series class.
• Added data_frame class, similar to Python Pandas DataFrame.
• Serialization and deserilization have been updated with more robust and reusable API, implemented in util.serialize() and util.deserialize() functions.
• Added matrix.ext.broadcast utility (similar to broadcast in numpy).
• Added ProbablisitcMatrixANNComponent, which allow to implement probabilistic mixtures of posteriors and/or likelihoods.
• Added batch normalization ANN component.
• Allowing matrix.join to add new axis.
• Added methods prod(), cumsum() and cumprod() at matrix classes.
• Added methods count_eq() and count_neq() at matrix classes.
• Serializable objects API have been augmented with methods ctor_name() and
  ctor_params() in Lua, refered to luaCtorName() and luaCtorParams() in C++.
• Added cast.to to dynamic cast C++ objects pushed into Lua, allowing to convert base class objects into any of its derived classes.
• Added matrix.sparse as valid values for targets in ann.loss.mse and
  ann.loss.cross_entropy.
• Changed matrix metamethods __index and __newindex, allowing to use
  matrix objects with standard Lua operator[].
• Added matrix.masked_fill and matrix.masked_copy matrix.
• Added matrix.indexed_fill and matrix.indexed_copy matrix.
• Added ann.components.probabilistic_matrix, and its corresponding specializations ann.components.left_probabilistic_matrix and
  ann.components.right_probabilistic_matrix.
• Added operator[] in the right side of matrix operations.
• Added ann.components.transpose.
• Added max_gradients_norm in traianble.supervised_trainer, to avoid gradients exploding.
• Added ann.components.actf.sparse_logistic a logistic activation function with sparsity penalty.
• Simplified math.add, math.sub, ... and other math extensions for reductions, their original behavior can be emulated by using bind function.
• Added bind function to freeze any positional argument of any Lua function.
• Function stats.boot uses multiple_unpack to allow a table of sizes and the generation of multiple index matrices.
• Added multiple_unpack Lua function.
• Added __tostring metamethod to numeric memory blocks in Lua.
• Added dataset.token.sparse_matrix, a dataset which allow to traverse by rows a sparse matrix instance.
• Added matrix.sparse.builders.dok, a builder which uses the Dictionary-of-Keys format to construct a sparse matrix from scratch.
• Added method data to numeric matrix classes.
• Added methods values, indices, first_index to sparse matrix class.
• Fixed bugs when reading bad formed CSV files.
• Fixed bugs at statistical distributions.
• FloatRGB bug solved on equal (+=, -=, ...) operators. This bug affected ImageRGB operations such as resize.
• Solved problems when chaining methods in Lua, some objects end to be garbage collected.
• Improved support of strings in auto-completion (rlcompleter package).
• Solved bug at SparseMatrix<T> when reading it from a file.
• Solved bug in Image<T>::rotate90_cw methods.
• Solved bug in SparseMatrix::toDense() method.

C/C++

• Better LuaTable accessors, using [] operator.
• Implementation of matrix __index, __newindex and __call metamethods in C++.
• Implementation of matProd(), matCumSum() and matCumProd() functions.
• Implementation of matCountEq() and matCountNeq() functions for
  Matrix<T>.
• Updated matrix_ext_operations.h to change API of matrix operations. All functions have been overloaded to accept an in-place operation and another version which receives a destination matrix.
• Adding iterators to language models.
• Added MatrixScalarMap2 which receives as input2 a SparaseMatrix instance. This functions needs to be generalized to work with CPU and CUDA.
• The method SparseMatrix<T>::fromDenseMatrix() uses a DOKBuilder object to build the sparse matrix.
• The conversion of a Matrix<T> into a SparseMatrix<T> has been changed from a constructor overload to the static method
  SparseMatrix<T>::fromDenseMatrix().
• Added support for IPyLua.
• Optimized matrix access for confusion matrix.
• Minor changes in class.lua.
• Improved binding to avoid multiple object copies when pushing C++ objects.
• Added Git commit hash and compilation time.