Projects running under linux.
Showing Items 1-20 of 286 on page 1 of 15: 1 2 3 4 5 6 Next Last

Logo Barista 0.3

by klemms - April 16, 2018, 17:13:03 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 1381 views, 316 downloads, 1 subscription

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

Changes:
  • 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

Logo Somoclu 1.7.5

by peterwittek - March 1, 2018, 23:30:34 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 39551 views, 7160 downloads, 3 subscriptions

About: Somoclu is a massively parallel implementation of self-organizing 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:
  • New: A Makefile for mingw to build on Windows.
  • Changed: PR #94 added a much more efficient sparse kernel.
  • Changed: boilerplate code for Julia greatly improved.
  • Changed: Code cleanup, pre-processor macros simplified.
  • Changed: Adapted to Seaborn API changes in plotting heatmaps.

Logo MLweb 1.2

by lauerfab - February 23, 2018, 15:40:27 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 16380 views, 3840 downloads, 3 subscriptions

About: MLweb is an open source project that aims at bringing machine learning capabilities into web pages and web applications, while maintaining all computations on the client side. It includes (i) a javascript library to enable scientific computing within web pages, (ii) a javascript library implementing machine learning algorithms for classification, regression, clustering and dimensionality reduction, (iii) a web application providing a matlab-like development environment.

Changes:
  • Add bibtex entry of corresponding Neurocomputing paper
  • Create javascript modules to avoid global scope pollution in web pages

Logo Armadillo library 8.400

by cu24gjf - February 20, 2018, 03:26:16 CET [ Project Homepage BibTeX Download ] 135133 views, 25716 downloads, 5 subscriptions

Rating Whole StarWhole StarWhole StarWhole StarEmpty Star
(based on 3 votes)

About: Armadillo is a high quality C++ linear algebra library, aiming towards a good balance between speed and ease of use. The function syntax is deliberately similar to MATLAB. Useful for algorithm development directly in C++, or quick conversion of research code into production environments (eg. software & hardware products).

Changes:
  • faster handling of band matrices by solve() and chol()
  • faster incremental construction of sparse matrices via element access operators
  • faster diagonal views in sparse matrices
  • faster handling of sparse matrices by repmat()
  • faster loading of CSV files
  • faster gmm_diag class, for Gaussian mixture models with diagonal covariance matrices
  • speedups via expanded use of OpenMP by many element-wise functions
  • expanded kron() to handle sparse matrices
  • expanded index_min() and index_max() to handle cubes
  • expanded SpMat to save/load sparse matrices in coord format
  • expanded .save() to allow appending new datasets to existing HDF5 files
  • expanded .save()/.load() to allow specification of datasets within HDF5 files
  • expanded .each_slice() to optionally use OpenMP for multi-threaded execution
  • expanded clamp() to handle cubes
  • added submatrix & subcube iterators
  • added normpdf(), normcdf(), mvnrnd()
  • added chi2rnd(), wishrnd(), iwishrnd()
  • added gmm_full class, for Gaussian mixture models with full covariance matrices
  • added affmul() to simplify application of affine transformations
  • added intersect() for finding common elements in two vectors/matrices

Logo DataDeps.jl v0.2.2

by oxinabox - February 8, 2018, 03:54:26 CET [ Project Homepage BibTeX Download ] 697 views, 247 downloads, 2 subscriptions

About: DataDeps is a package for simplifying the management of data in your julia application. In particular it is designed to make getting static data from some server into the local machine, and making programs know where that data is trivial.

Changes:

Initial Announcement on mloss.org.


Logo JMLR dlib ml 19.9

by davis685 - January 23, 2018, 01:48:45 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 225608 views, 34791 downloads, 5 subscriptions

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 removes the need for Boost.Python when using dlib via Python. This makes compiling the Python interface to dlib much easier as there are now no external dependencies.


Logo Indefinite Core Vector Machine 0.1

by fmschleif - January 5, 2018, 22:35:38 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 1641 views, 423 downloads, 3 subscriptions

About: Armadillo/C++ implementation of the Indefinite Core Vector Machine

Changes:

Some tiny errors in the Nystroem demo scripts - should be ok now Initial Announcement on mloss.org.


Logo WEKA 3.9.2

by mhall - December 22, 2017, 03:39:19 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 83849 views, 19306 downloads, 5 subscriptions

Rating Whole StarWhole StarWhole StarWhole StarEmpty Star
(based on 6 votes)

About: The Weka workbench contains a collection of visualization tools and algorithms for data analysis and predictive modelling, together with graphical user interfaces for easy access to this [...]

Changes:

This release include a lot of bug fixes and improvements. Some of these are detailed at

http://jira.pentaho.com/projects/DATAMINING/issues/DATAMINING-771

As usual, for a complete list of changes refer to the changelogs.


Logo Operator Discretization Library 0.6

by jonasadl - December 19, 2017, 15:24:08 CET [ Project Homepage BibTeX Download ] 694 views, 222 downloads, 2 subscriptions

About: Operator Discretization Library (ODL) is a Python library that enables research in inverse problems on realistic or real data.

Changes:

Initial Announcement on mloss.org.


Logo Aboleth 0.7

by dsteinberg - December 14, 2017, 02:39:19 CET [ Project Homepage BibTeX Download ] 3109 views, 911 downloads, 3 subscriptions

About: A bare-bones TensorFlow framework for Bayesian deep learning and Gaussian process approximation

Changes:

Release 0.7.0

  • Update to TensorFlow r1.4.

  • Tutorials in the documentation on:

  • Interfacing with Keras

  • Saving/loading models

  • How to build a variety of regressors with Aboleth

  • New prediction module with some convenience functions, including freezing the weight samples during prediction.

  • Bayesian convolutional layers with accompanying demo.

  • Allow the number of samples drawn from a model to be varied by using placeholders.

  • Generalise the feature embedding layers to work on matrix inputs (instead of just column vectors).

  • Numerous numerical and usability fixes.


Logo sparkcrowd 0.1.5

by enriquegrodrigo - December 13, 2017, 13:13:35 CET [ Project Homepage BibTeX Download ] 1834 views, 540 downloads, 3 subscriptions

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

Changes:

Changes: - Minor improvements in code and documentation


Logo Theano 1.0.1

by jaberg - December 7, 2017, 14:14:38 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 43193 views, 7302 downloads, 3 subscriptions

About: A Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Dynamically generates CPU and GPU modules for good performance. Deep Learning Tutorials illustrate deep learning with Theano.

Changes:

Theano 1.0.1 (6th of December, 2017)

This is a maintenance release of Theano, version 1.0.1, with no new features, but some important bug fixes.

Highlights (since 1.0.0):

  • Fixed compilation and improved float16 support for topK on GPU

  • NB: topK support on GPU is experimental and may not work for large input sizes on certain GPUs

  • Fixed cuDNN reductions when axes to reduce have size 1

  • Attempted to prevent re-initialization of the GPU in a child process

  • Fixed support for temporary paths with spaces in Theano initialization

  • Spell check pass on the documentation


Logo DFLsklearn, Hyperparameters optimization in Scikit Learn 0.1

by vlatorre - November 23, 2017, 13:14:36 CET [ Project Homepage BibTeX Download ] 902 views, 232 downloads, 1 subscription

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

Changes:

Initial Announcement on mloss.org.


Logo Obandit 0.2

by fre - November 6, 2017, 14:33:02 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 1323 views, 331 downloads, 2 subscriptions

About: Obandit is an Ocaml module for multi-armed bandits. It supports the EXP, UCB and Epsilon-greedy family of algorithms.

Changes:

Initial Announcement on mloss.org.


Logo AffectiveTweets 1.0.0

by felipebravom - November 1, 2017, 02:24:58 CET [ Project Homepage BibTeX Download ] 1107 views, 352 downloads, 3 subscriptions

About: A WEKA package for analyzing emotion and sentiment of tweets.

Changes:

Initial Announcement on mloss.org.


Logo HIERDENC 1.0

by billandreo - October 31, 2017, 16:01:32 CET [ Project Homepage BibTeX Download ] 2605 views, 2399 downloads, 2 subscriptions

About: This is a tool for retrieving nearest neighbors and clustering of large categorical data sets represented in transactional form. The clustering is achieved via a locality-sensitive hashing of categorical datasets for speed and scalability.

Changes:

Initial Announcement on mloss.org.


Logo Accord.NET Framework 3.8.0

by cesarsouza - October 23, 2017, 20:50:27 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 45400 views, 7639 downloads, 2 subscriptions

About: The Accord.NET Framework is a .NET machine learning framework combined with audio and image processing libraries completely written in C#. It is a complete framework for building production-grade computer vision, computer audition, signal processing and statistics applications even for commercial use. A comprehensive set of sample applications provide a fast start to get up and running quickly, and an extensive online documentation helps fill in the details.

Changes:

For a complete list of changes, please see the full release notes at the release details page at:

https://github.com/accord-net/framework/releases/tag/v3.8.0


Logo bufferkdtree 1.3

by fgieseke - October 20, 2017, 11:39:59 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 1037 views, 223 downloads, 2 subscriptions

About: The bufferkdtree package is a Python library that aims at accelerating nearest neighbor computations using both k-d trees and modern many-core devices such as graphics processing units (GPUs).

Changes:

Initial Announcement on mloss.org.


Logo JMLR Jstacs 2.3

by keili - September 13, 2017, 14:25:38 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 35598 views, 8145 downloads, 4 subscriptions

About: A Java framework for statistical analysis and classification of biological sequences

Changes:

New classes and packages:

  • Jstacs 2.3 is the first release to be accompanied by JstacsFX, a library for building JavaFX-based graphical user interfaces based on JstacsTools
  • new interface MultiThreadedFunction
  • new class LargeSequenceReader for reading large sequence files in chunks
  • new interface QuickScanningSequenceScore
  • new class RegExpValidator for checking String inputs against a regular expression
  • new class IUPACDNAAlphabet

New features and improvements:

  • Alignments may now handle different costs for insert and delete gaps
  • ListResults may now be constructed from Collections of ResultSets
  • Several minor improvements and bugfixes in many classes
  • Improvements of documentation of several classes

About: A non-iterative, incremental and hyperparameter-free learning method for one-layer feedforward neural networks without hidden layers. This method efficiently obtains the optimal parameters of the network, regardless of whether the data contains a greater number of samples than variables or vice versa. It does this by using a square loss function that measures errors before the output activation functions and scales them by the slope of these functions at each data point. The outcome is a system of linear equations that obtain the network's weights and that is further transformed using Singular Value Decomposition.

Changes:

Initial Announcement on mloss.org.


Showing Items 1-20 of 286 on page 1 of 15: 1 2 3 4 5 6 Next Last