Debian Science Project
Summary
Machine learning
paquets pour l’apprentissage automatique de Debian Science

Ce métapaquet installe les paquets utiles pour l’apprentissage automatique. Sont inclus des paquets allant des systèmes d’inférence basés sur la connaissance (expert) aux logiciels mettant en œuvre les méthodes sophistiquées de statistiques qui actuellement dominent le sujet.

Description

For a better overview of the project's availability as a Debian package, each head row has a color code according to this scheme:

If you discover a project which looks like a good candidate for Debian Science to you, or if you have prepared an unofficial Debian package, please do not hesitate to send a description of that project to the Debian Science mailing list

Links to other tasks

Debian Science Machine learning packages

Official Debian packages with high relevance

Autoclass
Classification automatique ou mise en grappe
Versions of package autoclass
ReleaseVersionArchitectures
bullseye3.3.6.dfsg.1-1amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
jessie3.3.6.dfsg.1-1amd64,armel,armhf,i386
stretch3.3.6.dfsg.1-1amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
buster3.3.6.dfsg.1-1amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
sid3.3.6.dfsg.1-1amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
squeeze3.3.6-1amd64,armel,i386,ia64,kfreebsd-amd64,kfreebsd-i386,mips,mipsel,powerpc,s390,sparc
wheezy3.3.6.dfsg.1-1amd64,armel,armhf,i386,ia64,kfreebsd-amd64,kfreebsd-i386,mips,mipsel,powerpc,s390,s390x,sparc
Debtags of package autoclass:
fieldmathematics
interfacecommandline
roleprogram
scopeutility
useorganizing
Popcon: 12 users (7 upd.)*
Versions and Archs
License: DFSG free

AutoClass résout le problème de la découverte automatique de classes dans des données (parfois appelé clustering ou unsupervised learning) à la différence de la génération de description de classes à partir d'exemples nommés (appelé supervised learning). Il essaie de découvrir des classes « naturelles » dans les données. AutoClass est applicable à des observations de choses qui peuvent être décrites par un ensemble d'attributs, sans se référer à d'autres éléments. Les valeurs de données correspondantes à chaque attribut sont limitées à être soit des nombres ou les éléments d'un ensemble fixé de symboles. Avec des données numériques, une erreur de mesure doit être fournie.

Caffe-cpu
cadriciel ouvert et rapide d'apprentissage profond –⋅méta-paquet
Versions of package caffe-cpu
ReleaseVersionArchitectures
sid1.0.0+git20180821.99bd997-4amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
bullseye1.0.0+git20180821.99bd997-4amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
buster1.0.0+git20180821.99bd997-2amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
stretch1.0.0~rc4-1amd64,arm64,armel,i386,mips,mips64el,mipsel,ppc64el,s390x
Popcon: 0 users (0 upd.)*
Versions and Archs
License: DFSG free
Git

Caffe est un cadriciel d'apprentissage profond créé avec le souci de l'expression, la vitesse et la modularité. Il est développé par le BAIR («⋅Berkeley AI Research Lab⋅») et des contributeurs de la communauté.

Ce méta-paquet fournit la version CPU_ONLY de caffe⋅:

 –⋅caffe-tools-cpu ;
 –⋅libcaffe-cpu* ;
 –⋅python3-caffe-cpu.
et suggère les paquets suivants⋅:
 –⋅libcaffe-cpu-dev ;
 –⋅caffe-doc.

Attention, cette version CPU_ONLY ne peut pas coexister avec la version CUDA.

Please cite: Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama and Trevor Darrell: Caffe: Convolutional Architecture for Fast Feature Embedding. (eprint) arXiv preprint arXiv:1408.5093 (2014)
Gprolog
GNU Prolog compiler
Maintainer: Salvador Abreu
Versions of package gprolog
ReleaseVersionArchitectures
squeeze1.3.0-6.1amd64,i386,mips,mipsel,powerpc,sparc
wheezy1.3.0-6.1amd64,i386,mips,mipsel,powerpc,sparc
jessie1.3.0-6.1amd64,i386
bullseye1.4.5.0-2amd64,i386
sid1.4.5.0-2amd64,i386
Debtags of package gprolog:
develcompiler, interpreter, lang:prolog
interfacecommandline
roleprogram
scopeutility
suitegnu
works-withsoftware:source
Popcon: 19 users (5 upd.)*
Versions and Archs
License: DFSG free

GNU Prolog is a free Prolog compiler with constraint solving over finite domains (FD). GNU Prolog is largely compliant with the ISO standard and is part of the Prolog Commons initiative.

This package contains the compiler and runtime system for the ISO standard version of GNU Prolog, including the prototype modules implementation.

Libcomplearn-dev
machine-learning through data compression development files
Maintainer: Rudi Cilibrasi
Versions of package libcomplearn-dev
ReleaseVersionArchitectures
squeeze1.1.6-1amd64,armel,i386,ia64,kfreebsd-amd64,kfreebsd-i386,mips,mipsel,powerpc,s390,sparc
squeeze1.1.6-1amd64,armel,i386,ia64,kfreebsd-amd64,kfreebsd-i386,mips,mipsel,powerpc,s390,sparc
Debtags of package libcomplearn-dev:
devellibrary, library
roledevel-lib, devel-lib
Popcon: 0 users (0 upd.)*
Versions and Archs
License: DFSG free
Hg

complearn is a library for parameter-free universal learning. Using this library, developers can access a wealth of powerful and general techniques in artificial intelligence and pattern recognition including fields such as genomics, language evolution, music recognition, and much more

Libcv-dev
Translation package for libcv-dev
Versions of package libcv-dev
ReleaseVersionArchitectures
wheezy-security2.3.1-11+deb7u4amd64,armel,armhf,i386
stretch2.4.9.1+dfsg1-2amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
jessie-security2.4.9.1+dfsg-1+deb8u2amd64,armel,armhf,i386
wheezy2.3.1-11+deb7u1amd64,armel,armhf,i386,ia64,kfreebsd-amd64,kfreebsd-i386,mips,mipsel,powerpc,s390,s390x,sparc
jessie2.4.9.1+dfsg-1+deb8u1amd64,armel,armhf,i386
squeeze2.1.0-3+squeeze1amd64,armel,i386,ia64,kfreebsd-amd64,kfreebsd-i386,mips,mipsel,powerpc,s390,sparc
Debtags of package libcv-dev:
devellibrary
roledevel-lib
Popcon: 19 users (16 upd.)*
Versions and Archs
License: DFSG free
Git

This package provide files for translation from libcv-dev to subdivided packages.

This package contains the header files and static library needed to compile applications that use OpenCV (Open Computer Vision).

The Open Computer Vision Library is a collection of algorithms and sample code for various computer vision problems. The library is compatible with IPL (Intel's Image Processing Library) and, if available, can use IPP (Intel's Integrated Performance Primitives) for better performance.

OpenCV provides low level portable data types and operators, and a set of high level functionalities for video acquisition, image processing and analysis, structural analysis, motion analysis and object tracking, object recognition, camera calibration and 3D reconstruction.

Please cite: Gary Bradski and Adrian Kaehler: Learning OpenCV: Computer Vision with the OpenCV Library (2008)
Registry entries: SciCrunch  OMICtools 
Libevocosm-dev
C++ framework for developing evolutionary algorithms
Maintainer: Al Stone (Chris Lamb)
Versions of package libevocosm-dev
ReleaseVersionArchitectures
squeeze3.1.0-3.1amd64,armel,i386,ia64,kfreebsd-amd64,kfreebsd-i386,mips,mipsel,powerpc,s390,sparc
wheezy4.0.2-2.1amd64,armel,armhf,i386,ia64,kfreebsd-amd64,kfreebsd-i386,mips,mipsel,powerpc,s390,s390x,sparc
stretch4.0.2-3.1amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
jessie4.0.2-3amd64,armel,armhf,i386
Debtags of package libevocosm-dev:
devellibrary
roledevel-lib
Popcon: 9 users (0 upd.)*
Versions and Archs
License: DFSG free

This library provides a framework for programming a wide variety of evolutionary algorithms, ranging from genetic algorithms to agent simulations. Evocosm is the foundation for Acovea

This package contains the files needed to develop code using libevocosm.

Libfann-dev
Development libraries and header files for FANN
Maintainer: Christian Kastner
Versions of package libfann-dev
ReleaseVersionArchitectures
stretch2.2.0+ds-3amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
jessie2.1.0~beta+dfsg-1amd64,armel,armhf,i386
wheezy2.1.0~beta~dfsg-8amd64,armel,armhf,i386,ia64,kfreebsd-amd64,kfreebsd-i386,mips,mipsel,powerpc,s390,s390x,sparc
squeeze2.1.0~beta~dfsg-2amd64,armel,i386,ia64,kfreebsd-amd64,kfreebsd-i386,mips,mipsel,powerpc,s390,sparc
sid2.2.0+ds-6amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
buster2.2.0+ds-5amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
bullseye2.2.0+ds-6amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
Debtags of package libfann-dev:
devellang:c, library
roledevel-lib
Popcon: 14 users (20 upd.)*
Versions and Archs
License: DFSG free
Git

Fast Artificial Neural Network Library is a free open source neural network library, which implements multilayer artificial neural networks in C with support for both fully connected and sparsely connected networks. Cross-platform execution in both fixed and floating point are supported. It includes a framework for easy handling of training data sets. It is easy to use, versatile, well documented, and fast.

This package contains the header files and static libraries which are needed for developing libfann applications.

Libga-dev
C++ Library of Genetic Algorithm Components
Versions of package libga-dev
ReleaseVersionArchitectures
sid2.4.7-4amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
bullseye2.4.7-4amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
buster2.4.7-4amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
stretch2.4.7-4amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
jessie2.4.7-3.1amd64,armel,armhf,i386
wheezy2.4.7-3amd64,armel,armhf,i386,ia64,kfreebsd-amd64,kfreebsd-i386,mips,mipsel,powerpc,s390,s390x,sparc
squeeze2.4.7-3amd64,armel,i386,ia64,kfreebsd-amd64,kfreebsd-i386,mips,mipsel,powerpc,s390,sparc
Debtags of package libga-dev:
devellibrary
roledevel-lib
Popcon: 12 users (5 upd.)*
Versions and Archs
License: DFSG free

GAlib contains a set of C++ genetic algorithm objects. The library includes tools for using genetic algorithms to do optimization in any C++ program using any representation and genetic operators. The documentation includes an extensive overview of how to implement a genetic algorithm as well as examples illustrating customizations to the GAlib classes.

This package contains the development files.

Liblinear-dev
Development libraries and header files for LIBLINEAR
Maintainer: Christian Kastner
Versions of package liblinear-dev
ReleaseVersionArchitectures
bullseye2.3.0+dfsg-2amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
wheezy1.8+dfsg-1amd64,armel,armhf,i386,ia64,kfreebsd-amd64,kfreebsd-i386,mips,mipsel,powerpc,s390,s390x,sparc
squeeze1.6+dfsg-1amd64,armel,i386,ia64,kfreebsd-amd64,kfreebsd-i386,mips,mipsel,powerpc,s390,sparc
jessie1.8+dfsg-4amd64,armel,armhf,i386
stretch2.1.0+dfsg-2amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
buster2.1.0+dfsg-4amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
sid2.3.0+dfsg-2amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
Debtags of package liblinear-dev:
devellang:c, lang:c++, library
roledevel-lib
Popcon: 11 users (13 upd.)*
Versions and Archs
License: DFSG free
Git

LIBLINEAR is a library for learning linear classifiers for large scale applications. It supports Support Vector Machines (SVM) with L2 and L1 loss, logistic regression, multi class classification and also Linear Programming Machines (L1-regularized SVMs). Its computational complexity scales linearly with the number of training examples making it one of the fastest SVM solvers around.

This package contains the header files and static libraries.

Libmlpack-dev
intuitive, fast, scalable C++ machine learning library (development libs)
Versions of package libmlpack-dev
ReleaseVersionArchitectures
jessie1.0.10-1amd64,armel,armhf,i386
buster3.0.4-1amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
sid3.2.1-1armel,i386
stretch2.1.1-1amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
sid3.2.2-1amd64,arm64,armhf,mips64el,mipsel,ppc64el,s390x
Popcon: 2 users (1 upd.)*
Versions and Archs
License: DFSG free
Git

This package contains the mlpack Library development files.

Machine Learning Pack (mlpack) is an intuitive, fast, scalable C++ machine learning library, meant to be a machine learning analog to LAPACK. It aims to implement a wide array of machine learning methods and function as a "swiss army knife" for machine learning researchers.

Libocas-dev
Development libraries and header files for LIBOCAS
Maintainer: Christian Kastner
Versions of package libocas-dev
ReleaseVersionArchitectures
wheezy0.93-1amd64,armel,armhf,i386,ia64,kfreebsd-amd64,kfreebsd-i386,mips,mipsel,powerpc,s390,s390x,sparc
stretch0.97+dfsg-3amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
squeeze0.93-1amd64,armel,i386,ia64,kfreebsd-amd64,kfreebsd-i386,mips,mipsel,powerpc,s390,sparc
bullseye0.97+dfsg-6amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
sid0.97+dfsg-6amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
jessie0.97-1amd64,armel,armhf,i386
buster0.97+dfsg-5amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
Debtags of package libocas-dev:
devellang:c, library
roledevel-lib
Popcon: 6 users (7 upd.)*
Versions and Archs
License: DFSG free
Git

This library implements Optimized Cutting Plane Algorithm (OCAS) for training linear Support Vector Machine (SVM) classifiers from large-scale data. The computational effort of OCAS scales linearly with the number of training examples. It is one of the fastest SVM solvers around for solving linear and multiclass L2 regularized SVMs.

This package contains the header files and static libraries.

Libqsearch-dev
nondeterministic quartet tree search library for unrooted trees
Versions of package libqsearch-dev
ReleaseVersionArchitectures
squeeze1.0.8-3amd64,armel,i386,ia64,kfreebsd-amd64,kfreebsd-i386,mips,mipsel,powerpc,s390,sparc
Popcon: 0 users (0 upd.)*
Versions and Archs
License: DFSG free
Svn

qsearch is a library for universal hierarchical clustering using an arbitrary distance matrix as input. It searches through the space of all possible unrooted trees of a given size and finds the closest match based on a weighted quartet cost function determined by the distance matrix. When used in combination with other feature extraction libraries such as libcomplearn this system can be used for fast and accurate phylogenetic reconstruction, linguistic analysis, or stemmatology.

Libroot-math-mlp-dev
Multi layer perceptron extension for ROOT - development files
Versions of package libroot-math-mlp-dev
ReleaseVersionArchitectures
wheezy5.34.00-2amd64,armel,armhf,i386,ia64,kfreebsd-amd64,kfreebsd-i386,mips,mipsel,sparc
jessie5.34.19+dfsg-1.2amd64,armel,armhf,i386
Debtags of package libroot-math-mlp-dev:
devellibrary
roledevel-lib
Popcon: 5 users (0 upd.)*
Versions and Archs
License: DFSG free
Git

The ROOT system provides a set of OO frameworks with all the functionality needed to handle and analyze large amounts of data efficiently.

This package contains development files of the mlp plug-in for ROOT, provides a Multi Layer Perceptron Neural Network package for ROOT.

Libroot-montecarlo-vmc-dev
Virtual Monte-Carlo library for ROOT - development files
Versions of package libroot-montecarlo-vmc-dev
ReleaseVersionArchitectures
wheezy5.34.00-2amd64,armel,armhf,i386,ia64,kfreebsd-amd64,kfreebsd-i386,mips,mipsel,sparc
jessie5.34.19+dfsg-1.2amd64,armel,armhf,i386
Debtags of package libroot-montecarlo-vmc-dev:
devellibrary
roledevel-lib
Popcon: 5 users (0 upd.)*
Versions and Archs
License: DFSG free
Git

The ROOT system provides a set of OO frameworks with all the functionality needed to handle and analyze large amounts of data efficiently.

This package contains development files of the Vmc library for ROOT.

Libroot-tmva-dev
Toolkit for multivariate data analysis - development files
Versions of package libroot-tmva-dev
ReleaseVersionArchitectures
jessie5.34.19+dfsg-1.2amd64,armel,armhf,i386
wheezy5.34.00-2amd64,armel,armhf,i386,ia64,kfreebsd-amd64,kfreebsd-i386,mips,mipsel,sparc
Debtags of package libroot-tmva-dev:
devellibrary
roledevel-lib
Popcon: 5 users (0 upd.)*
Versions and Archs
License: DFSG free
Git

The ROOT system provides a set of OO frameworks with all the functionality needed to handle and analyze large amounts of data efficiently.

The Toolkit for Multivariate Analysis (TMVA) provides a ROOT-integrated environment for the parallel processing and evaluation of MVA techniques to discriminate signal from background samples. It presently includes (ranked by complexity):

  • Rectangular cut optimisation
  • Correlated likelihood estimator (PDE approach)
  • Multi-dimensional likelihood estimator (PDE - range-search approach)
  • Fisher (and Mahalanobis) discriminant
  • H-Matrix (chi-squared) estimator
  • Artificial Neural Network (two different implementations)
  • Boosted Decision Trees

The TMVA package includes an implementation for each of these discrimination techniques, their training and testing (performance evaluation). In addition all these methods can be tested in parallel, and hence their performance on a particular data set may easily be compared.

This package provides development files of TMVA package for ROOT.

Libshark-dev
development files for Shark
Versions of package libshark-dev
ReleaseVersionArchitectures
stretch3.1.3+ds1-2amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
Popcon: 5 users (0 upd.)*
Versions and Archs
License: DFSG free
Git

Shark is a modular C++ library for the design and optimization of adaptive systems. It provides methods for linear and nonlinear optimization, in particular evolutionary and gradient-based algorithms, kernel-based learning algorithms and neural networks, and various other machine learning techniques.

This package provides the development files.

Please cite: C. Igel, V. Heidrich-Meisner and T. Glasmachers: Shark (eprint) Journal of Machine Learning Research 9:993-996 (2008)
Libshogun-dev
Large Scale Machine Learning Toolbox
Versions of package libshogun-dev
ReleaseVersionArchitectures
jessie3.2.0-7.3amd64,armel,armhf,i386
squeeze0.9.3-4amd64,armel,i386,ia64,kfreebsd-amd64,kfreebsd-i386,mips,mipsel,powerpc,s390,sparc
buster3.2.0-8amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
sid3.2.0-9amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
Debtags of package libshogun-dev:
devellibrary
roledevel-lib
Popcon: 4 users (5 upd.)*
Versions and Archs
License: DFSG free
Svn

SHOGUN - is a new machine learning toolbox with focus on large scale kernel methods and especially on Support Vector Machines (SVM) with focus to bioinformatics. It provides a generic SVM object interfacing to several different SVM implementations. Each of the SVMs can be combined with a variety of the many kernels implemented. It can deal with weighted linear combination of a number of sub-kernels, each of which not necessarily working on the same domain, where an optimal sub-kernel weighting can be learned using Multiple Kernel Learning. Apart from SVM 2-class classification and regression problems, a number of linear methods like Linear Discriminant Analysis (LDA), Linear Programming Machine (LPM), (Kernel) Perceptrons and also algorithms to train hidden markov models are implemented. The input feature-objects can be dense, sparse or strings and of type int/short/double/char and can be converted into different feature types. Chains of preprocessors (e.g. substracting the mean) can be attached to each feature object allowing for on-the-fly pre-processing.

SHOGUN comes in different flavours, a stand-a-lone version and also with interfaces to Matlab(tm), R, Octave, Readline and Python. This package includes the developer files required to create stand-a-lone executables.

Libsvm-dev
LIBSVM header files
Maintainer: Chen-Tse Tsai (Adrian Bunk)
Versions of package libsvm-dev
ReleaseVersionArchitectures
sid3.21+ds-1.2amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
bullseye3.21+ds-1.2amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
squeeze2.91-1amd64,armel,i386,ia64,kfreebsd-amd64,kfreebsd-i386,mips,mipsel,powerpc,s390,sparc
stretch3.21+ds-1.1amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
jessie3.12-1amd64,armel,armhf,i386
wheezy3.12-1amd64,armel,armhf,i386,ia64,kfreebsd-amd64,kfreebsd-i386,mips,mipsel,powerpc,s390,s390x,sparc
buster3.21+ds-1.2amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
upstream3.24
Debtags of package libsvm-dev:
devellibrary
roledevel-lib
Popcon: 21 users (20 upd.)*
Newer upstream!
License: DFSG free

LIBSVM, a machine-learning library, is an easy-to-use package for support vector classification, regression and one-class SVM. It supports multi-class classification, probability outputs, and parameter selection.

This package contains the development header files.

Libtorch3-dev
State of the art machine learning library - development files
Versions of package libtorch3-dev
ReleaseVersionArchitectures
sid3.1-2.2amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
bullseye3.1-2.2amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
buster3.1-2.2amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
stretch3.1-2.2amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
jessie3.1-2.1amd64,armel,armhf,i386
wheezy3.1-2.1amd64,armel,armhf,i386,ia64,kfreebsd-amd64,kfreebsd-i386,mips,mipsel,powerpc,s390,s390x,sparc
squeeze3.1-2.1amd64,armel,i386,ia64,kfreebsd-amd64,kfreebsd-i386,mips,mipsel,powerpc,s390,sparc
Debtags of package libtorch3-dev:
devellibrary
roledevel-lib
Popcon: 16 users (7 upd.)*
Versions and Archs
License: DFSG free

Torch is a machine-learning library, written in C++. Its aim is to provide the state-of-the-art of the best algorithms.

  • Many gradient-based methods, including multi-layered perceptrons, radial basis functions, and mixtures of experts. Many small "modules" (Linear module, Tanh module, SoftMax module, ...) can be plugged together.
  • Support Vector Machine, for classification and regression.
  • Distribution package, includes Kmeans, Gaussian Mixture Models, Hidden Markov Models, and Bayes Classifier, and classes for speech recognition with embedded training.
  • Ensemble models such as Bagging and Adaboost.
  • Non-parametric models such as K-nearest-neighbors, Parzen Regression and Parzen Density Estimator.

This package is the Torch development package (header files and static library.)

Libvigraimpex-dev
development files for the C++ computer vision library
Versions of package libvigraimpex-dev
ReleaseVersionArchitectures
squeeze1.7.0+dfsg-7amd64,armel,i386,ia64,kfreebsd-amd64,kfreebsd-i386,mips,mipsel,powerpc,s390,sparc
wheezy1.7.1+dfsg1-3amd64,armel,armhf,i386,ia64,kfreebsd-amd64,kfreebsd-i386,mips,mipsel,powerpc,s390,s390x,sparc
jessie1.9.0+dfsg-10amd64,armel,armhf,i386
stretch1.10.0+git20160211.167be93+dfsg-2amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
buster1.10.0+git20160211.167be93+dfsg1-2amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
bullseye1.10.0+git20160211.167be93+dfsg1-2amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
sid1.10.0+git20160211.167be93+dfsg1-2amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
experimental1.11.1+dfsg-2amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
upstream1.11.1
Debtags of package libvigraimpex-dev:
devellang:c++, library
roledevel-lib
works-withimage, image:raster
Popcon: 14 users (9 upd.)*
Newer upstream!
License: DFSG free
Git

Vision with Generic Algorithms (VIGRA) is a computer vision library that puts its main emphasis on flexible algorithms, because algorithms represent the principle know-how of this field. The library was consequently built using generic programming as introduced by Stepanov and Musser and exemplified in the C++ Standard Template Library. By writing a few adapters (image iterators and accessors) you can use VIGRA's algorithms on top of your data structures, within your environment.

This package contains the header and development files needed to build programs and packages using VIGRA.

Lua-torch-graph
Graph Computation Package for Torch Framework
Versions of package lua-torch-graph
ReleaseVersionArchitectures
buster0~20161121-g37dac07-3all
Popcon: 0 users (0 upd.)*
Versions and Archs
License: DFSG free
Git

This package provides graphical computation for Torch.

This package also ships a graphviz interface, you need not graphviz to be able to use this library but, if you have it, you will be able to display the graphs that you have created.

Lua-torch-image
Image Load/Save Library for Torch Framework
Versions of package lua-torch-image
ReleaseVersionArchitectures
buster0~20170420-g5aa1881-7amd64,armel,armhf,ppc64el
Popcon: 0 users (0 upd.)*
Versions and Archs
License: DFSG free
Git

"image" is the Torch7 distribution package for processing images. It contains a wide variety of functions divided into the following categories:

  • Saving and loading images as JPEG, PNG, PPM and PGM;
  • Simple transformations like translation, scaling and rotation;
  • Parameterized transformations like convolutions and warping;
  • Simple Drawing Routines like drawing text or a rectangle on an image;
  • Graphical user interfaces like display and window;
  • Color Space Conversions from and to RGB, YUV, Lab, and HSL;
  • Tensor Constructors for creating Lenna, Fabio and Gaussian and Laplacian kernels;

Note that unless specified otherwise, this package deals with images of size nChannel x height x width.

Lua-torch-nn
Neural Network Package for Torch Framework
Versions of package lua-torch-nn
ReleaseVersionArchitectures
buster0~20171002-g8726825+dfsg-4all
Popcon: 0 users (0 upd.)*
Versions and Archs
License: DFSG free
Git

This package provides an easy and modular way to build and train simple or complex neural networks using Torch Framework:

  • Modules are the bricks used to build neural networks. Each are themselves neural networks, but can be combined with other networks using containers to create complex neural networks:

  • Module: abstract class inherited by all modules.

  • Containers: container classes.
  • Transfer functions: non-linear functions.
  • Simple layers: simple network layer like Linear.
  • Table layers: layers for manipulating tables.
  • Convolution layers: several kinds of convolutions.

  • Criterions compute a gradient according to a given loss function given an input and a target:

  • Criterions: a list of all criterions.

  • MSECriterion: the Mean Squared Error criterion used for regression;
  • ClassNLLCriterion: the Negative Log Likelihood criterion used for classification.

  • Additional documentation:

  • Overview of the package essentials including modules, containers and training.

  • Training: how to train a neural network using optim.
  • Testing: how to test your modules.
  • Experimental Modules: a package containing experimental modules and criteria.

This package is a core part of the Torch Framework.

Lua-torch-nngraph
Neural Network Graph Package for Torch Framework
Versions of package lua-torch-nngraph
ReleaseVersionArchitectures
buster0~20170208-g3ed3b9b-3all
Popcon: 0 users (0 upd.)*
Versions and Archs
License: DFSG free
Git

This package provides graphical computation for nn library in Torch. The aim of this library is to provide users of nn package with tools to easily create complicated architectures. Any given nn module is going to be bundled into a graph node. The __call__ operator of an instance of nn.Module is used to create architectures as if one is writing function calls.

Lua-torch-optim
Numeric Optimization Package for Torch Framework
Versions of package lua-torch-optim
ReleaseVersionArchitectures
buster0~20171127-ga5ceed7-1all
Popcon: 0 users (0 upd.)*
Versions and Archs
License: DFSG free
Git

This package contains several optimization routines and a logger for Torch.

The following algorithms are provided:

  • Stochastic Gradient Descent
  • Averaged Stochastic Gradient Descent
  • L-BFGS
  • Congugate Gradients
  • AdaDelta
  • AdaGrad
  • Adam
  • AdaMax
  • FISTA with backtracking line search
  • Nesterov's Accelerated Gradient method
  • RMSprop
  • Rprop
  • CMAES All these algorithms are designed to support batch optimization as well as stochastic optimization. It's up to the user to construct an objective function that represents the batch, mini-batch, or single sample on which to evaluate the objective.

This package provides also logging and live plotting capabilities via the optim.Logger() function. Live logging is essential to monitor the network accuracy and cost function during training and testing, for spotting under- and over-fitting, for early stopping or just for monitoring the health of the current optimisation task.

Lua-torch-trepl
REPL Package for Torch Framework
Versions of package lua-torch-trepl
ReleaseVersionArchitectures
buster0~20170619-ge5e17e3-7amd64,armel,armhf,i386,ppc64el
Popcon: 5 users (5 upd.)*
Versions and Archs
License: DFSG free
Git

A pure Lua REPL (Read,Eval,Print-Loop) for LuaJIT, with heavy support for Torch types. It uses Readline for tab completion.

This package contains backend files to support the command line frontend 'th'.

Lua-torch-xlua
Lua Extension Package for Torch Framework
Versions of package lua-torch-xlua
ReleaseVersionArchitectures
buster0~20160719-g41308fe-7all
Popcon: 5 users (5 upd.)*
Versions and Archs
License: DFSG free
Git

Lua is pretty compact in terms of built-in functionalities: this package extends the table and string libraries, and provide other general purpose tools (progress bar, ...).

This package ships a set of useful extensions to Lua for Torch Framework.

Mcl
algorithme de Markov pour les grappes
Versions of package mcl
ReleaseVersionArchitectures
bullseye14-137+ds-4amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
sid14-137+ds-4amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
buster14-137+ds-3amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
stretch14-137-1amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
jessie14-137-1amd64,armel,armhf,i386
wheezy12-068-1amd64,armel,armhf,i386,ia64,kfreebsd-amd64,kfreebsd-i386,mips,mipsel,powerpc,s390,s390x,sparc
squeeze10-148-1amd64,armel,i386,ia64,kfreebsd-amd64,kfreebsd-i386,mips,mipsel,powerpc,s390,sparc
Debtags of package mcl:
fieldmathematics
roleprogram
Popcon: 25 users (13 upd.)*
Versions and Archs
License: DFSG free
Git

Le paquet MCL implémente l'algorithme MCL (Markov Cluster Algorithm) et fournit les utilitaires nécessaires à la manipulation de matrices creuses (type de matrices essentielles dans l'algorithme MCL) et à la réalisation d’expérimentations dans les grappes.

MCL est utilisé dans divers domaines comme en biologie (détection de familles de protéines, génomiques), en informatique (grappe de nœuds dans un réseau pair à pair) ou la linguistique (analyse de textes).

The package is enhanced by the following packages: zoem
Please cite: Stijn van Dongen and Cei Abreu-Goodger: Using MCL to extract clusters from networks. (PubMed,eprint) Methods Mol Biol. 804:281-95 (2012)
Registry entries: Bio.tools 
Octave-ga
code d'optimisation génétique pour Octave
Versions of package octave-ga
ReleaseVersionArchitectures
bullseye0.10.1-1all
buster0.10.0-6all
sid0.10.1-1all
squeeze0.9.7-1all
stretch0.10.0-2all
jessie0.10.0-2all
wheezy0.10.0-1all
Debtags of package octave-ga:
devellang:octave, library
roledevel-lib
Popcon: 0 users (0 upd.)*
Versions and Archs
License: DFSG free
Git

Ce paquet fournit une fonction pour travailler avec des algorithmes génétiques dans Octave, un logiciel de calcul numérique. Il offre la fonction ga(), qui fonctionne de la même manière que d'autres fonctions d'optimisation dans Octave.

Ce paquet de greffon d’Octave fait partie du projet Octave-Forge.

Pgapack
General-purpose genetic algorithm package
Maintainer: Dirk Eddelbuettel
Versions of package pgapack
ReleaseVersionArchitectures
wheezy1.1.1-3amd64,armel,armhf,i386,ia64,kfreebsd-amd64,kfreebsd-i386,mips,mipsel,powerpc,s390,s390x,sparc
squeeze1.1.1-1amd64,armel,i386,ia64,kfreebsd-amd64,kfreebsd-i386,mips,mipsel,powerpc,s390,sparc
jessie1.1.1-3amd64,armel,armhf,i386
Debtags of package pgapack:
fieldmathematics
Popcon: 0 users (0 upd.)*
Versions and Archs
License: DFSG free

PGAPack is a general-purpose, data-structure-neutral, parallel genetic algorithm package being developed at Argonne National Laboratory.

This package contains header files, manual pages, examples and tests. To use pgpack, you need to install the libpgapack-serial ('single cpu') or libpgapack-mpi ('parallel') package.

Screenshots of package pgapack
Python3-amp
Atomistic Machine-learning Package (python 3)
Versions of package python3-amp
ReleaseVersionArchitectures
bullseye0.6.1-1amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
buster0.6.1-1amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
sid0.6.1-1amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
Popcon: 20 users (1 upd.)*
Versions and Archs
License: DFSG free
Git

Amp is an open-source package designed to easily bring machine-learning to atomistic calculations. This project is being developed at Brown University in the School of Engineering, primarily by Andrew Peterson and Alireza Khorshidi, and is released under the GNU General Public License. Amp allows for the modular representation of the potential energy surface, allowing the user to specify or create descriptor and regression methods.

Amp is designed to integrate closely with the Atomic Simulation Environment (ASE). As such, the interface is in pure python, although several compute-heavy parts of the underlying code also have fortran versions to accelerate the calculations. The close integration with ASE means that any calculator that works with ASE ─ including EMT, GPAW, DACAPO, VASP, NWChem, and Gaussian ─ can easily be used as the parent method.

This package provides the python 3 modules.

Python3-genetic
genetic algorithms in Python
Versions of package python3-genetic
ReleaseVersionArchitectures
sid0.1.1b+git20170527.98255cb-1all
Popcon: users ( upd.)*
Versions and Archs
License: DFSG free
Git

Python3-genetic provides genetic algorithms for Python3, as often used in artificial intelligence. It should be able to solve any problem that consists in minimizing functions.

You'll find some demos using Genetic in this package, including an impressively simple program that provides a solution to the well-known TSP (Travelling Salesman Problem). Also, make sure to read demo/genetic_demo_2.py for the list of the special "magic" genes that make Genetic really fun and ... living !

Python3-keras
deep learning framework running on Theano or TensorFlow
Versions of package python3-keras
ReleaseVersionArchitectures
bullseye2.2.4-1all
buster2.2.4-1all
sid2.2.4-1all
upstream2.3.1
Popcon: 29 users (22 upd.)*
Newer upstream!
License: DFSG free
Git

Keras is a Python library for machine learning based on deep (multi- layered) artificial neural networks (DNN), which follows a minimalistic and modular design with a focus on fast experimentation.

Features of DNNs like neural layers, cost functions, optimizers, initialization schemes, activation functions and regularization schemes are available in Keras a standalone modules which can be plugged together as wanted to create sequence models or more complex architectures. Keras supports convolutions neural networks (CNN, used for image recognition resp. classification) and recurrent neural networks (RNN, suitable for sequence analysis like in natural language processing).

It runs as an abstraction layer on the top of Theano (math expression compiler) by default, which makes it possible to accelerate the computations by using (GP)GPU devices. Alternatively, Keras could run on Google's TensorFlow (not yet available in Debian).

Python3-lasagne
deep learning library build on the top of Theano (Python3 modules)
Versions of package python3-lasagne
ReleaseVersionArchitectures
stretch0.1+git20160728.8b66737-2all
bullseye0.1+git20181019.a61b76f-2all
sid0.1+git20181019.a61b76f-2all
buster0.1+git20181019.a61b76f-1all
Popcon: 6 users (9 upd.)*
Versions and Archs
License: DFSG free
Git

Lasagne is a Python library to build and train deep (multi-layered) artificial neural networks on the top of Theano (math expression compiler). In comparison to other abstraction layers for that like e.g. Keras, it abstracts Theano as little as possible.

Lasagne supports networks like Convolutional Neural Networks (CNN, mostly used for image recognition resp. classification) and the Long Short-Term Memory type (LSTM, a subtype of Recurrent Neural Networks, RNN).

This package contains the modules for Python 3.

Python3-mdp
Modular toolkit for Data Processing
Maintainer: Tiziano Zito
Versions of package python3-mdp
ReleaseVersionArchitectures
sid3.5-1all
jessie3.3-2all
wheezy3.3-1all
stretch3.5-1all
Popcon: 8 users (2 upd.)*
Versions and Archs
License: DFSG free
Git

Python data processing framework for building complex data processing software by combining widely used machine learning algorithms into pipelines and networks. Implemented algorithms include: Principal Component Analysis (PCA), Independent Component Analysis (ICA), Slow Feature Analysis (SFA), Independent Slow Feature Analysis (ISFA), Growing Neural Gas (GNG), Factor Analysis, Fisher Discriminant Analysis (FDA), and Gaussian Classifiers.

This package contains MDP for Python 3.

The package is enhanced by the following packages: python3-sklearn
Python3-opencv
liaisons Python⋅3 pour la bibliothèque de vision par ordinateur
Versions of package python3-opencv
ReleaseVersionArchitectures
sid4.1.2+dfsg-5amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
buster3.2.0+dfsg-6amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
bullseye4.1.2+dfsg-5amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
Popcon: 82 users (281 upd.)*
Versions and Archs
License: DFSG free
Git

Ce paquet fournit les liaisons Python⋅3 pour la bibliothèque OpenCV (Open Computer Vision).

La bibliothèque Open Computer Vision est un ensemble d'algorithmes et de codes d'exemple pour divers problèmes de vision par ordinateur. La bibliothèque est compatible avec IPL (Image Processing Library d'Intel) et, s'il est disponible, IPP d'Intel (Integrated Performance Primitives) pour de meilleures performances.

OpenCV fournit des types de données et des opérateurs portables de bas niveau et un ensemble de fonctionnalités de haut niveau pour l'acquisition vidéo, le traitement et l'analyse d'image, l'analyse structurale, l'analyse du mouvement et le suivi d'objet, la reconnaissance d’objet, la calibration de caméra et la reconstruction⋅3D.

Please cite: Gary Bradski and Adrian Kaehler: Learning OpenCV: Computer Vision with the OpenCV Library (2008)
Registry entries: SciCrunch  OMICtools 
Python3-sklearn
Python modules for machine learning and data mining - Python 3
Versions of package python3-sklearn
ReleaseVersionArchitectures
stretch0.18-5all
bullseye0.20.3+dfsg-0.1all
sid0.20.3+dfsg-0.1all
buster0.20.2+dfsg-6all
upstream0.22rc2.post1
Popcon: 480 users (96 upd.)*
Newer upstream!
License: DFSG free
Git

scikit-learn is a collection of Python modules relevant to machine/statistical learning and data mining. Non-exhaustive list of included functionality:

  • Gaussian Mixture Models
  • Manifold learning
  • kNN
  • SVM (via LIBSVM)

This package contains the Python 3 version.

The package is enhanced by the following packages: python3-sklearn-pandas
Python3-statsmodels
Python3 module for the estimation of statistical models
Versions of package python3-statsmodels
ReleaseVersionArchitectures
sid0.10.1-5all
buster0.8.0-9all
bullseye0.9.0-6all
upstream0.10.2
Popcon: 19 users (41 upd.)*
Newer upstream!
License: DFSG free
Git

statsmodels Python3 module provides classes and functions for the estimation of several categories of statistical models. These currently include linear regression models, OLS, GLS, WLS and GLS with AR(p) errors, generalized linear models for several distribution families and M-estimators for robust linear models. An extensive list of result statistics are available for each estimation problem.

Please cite: Skipper Seabold and Josef Perktold: Statsmodels: Econometric and statistical modeling with python (eprint) (2010)
Python3-thinc
Practical Machine Learning for NLP in Python
Versions of package python3-thinc
ReleaseVersionArchitectures
buster6.12.1-1amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
sid6.12.1-1amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
bullseye6.12.1-1amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
upstream7.3.1
Popcon: 2 users (6 upd.)*
Newer upstream!
License: DFSG free
Git

Thinc is the machine learning library powering spaCy https://spacy.io. It features a battle-tested linear model designed for large sparse learning problems, and a flexible neural network model under development for spaCy v2.0 https://spacy.io/usage/v2.

Thinc is a practical toolkit for implementing models that follow the "Embed, encode, attend, predict" architecture. It's designed to be easy to install, efficient for CPU usage and optimised for NLP and deep learning with text – in particular, hierarchically structured input and variable-length sequences.

R-cran-amore
GNU R: A MORE flexible neural network package
Versions of package r-cran-amore
ReleaseVersionArchitectures
sid0.2-15-3amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
bullseye0.2-15-3amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
wheezy0.2-12-2amd64,armel,armhf,i386,ia64,kfreebsd-amd64,kfreebsd-i386,mips,mipsel,powerpc,s390,s390x,sparc
jessie0.2-15-1amd64,armel,armhf,i386
stretch0.2-15-1amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
squeeze0.2-12-2amd64,armel,i386,ia64,kfreebsd-amd64,kfreebsd-i386,mips,mipsel,powerpc,s390,sparc
buster0.2-15-3amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
Popcon: 30 users (5 upd.)*
Versions and Archs
License: DFSG free
Git

This package was born to release the TAO robust neural network algorithm to the R users. It has grown and can be of interest for the users wanting to implement their own training algorithms as well as for those others whose needs lye only in the "user space".

R-cran-bayesm
paquet de GNU R pour l’inférence bayésienne
Versions of package r-cran-bayesm
ReleaseVersionArchitectures
bullseye3.1-4+dfsg-1amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
sid3.1-4+dfsg-1amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
stretch3.0-2-2amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
squeeze2.2-2-1amd64,armel,i386,ia64,kfreebsd-amd64,kfreebsd-i386,mips,mipsel,powerpc,s390,sparc
wheezy2.2-4-1amd64,armel,armhf,i386,ia64,kfreebsd-amd64,kfreebsd-i386,mips,mipsel,powerpc,s390,s390x,sparc
jessie2.2-5-1amd64,armel,armhf,i386
buster3.1-1-1amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
Debtags of package r-cran-bayesm:
fieldmathematics, statistics
suitegnu
Popcon: 43 users (12 upd.)*
Versions and Archs
License: DFSG free
Git

Le paquet bayesm couvre plusieurs modèles importants d’applications de commerce et de microéconométrie. Ce paquet comprend :

 – régression bayésienne (dep var univariés ou multivariés) ;
 – logit multinomial (MNL) et probit multinomial (MNP) ;
 – probit multivarié ;
 – normale multivariée de mélange ;
 – modèle linéaire hiérarchique avec probabilité normale et covariables ;
 – logit multinomial hiérarchique avec mélange de probabilités normales et
   de covariables ;
 – analyse bayésienne de données conjointes basées sur le choix ;
 – traitement bayésien de modèles linéaires de variables instrumentales ;
 – analyse de données d’enquête ordinales multivariées avec hétérogénéité
   d’utilisation d’échelle (comme dans Rossi et al., JASA (01)).

Pour plus de référence, consulter le livre des auteurs « Bayesian Statistics and Marketing » par Allenby, McCulloch et Rossi.

R-cran-class
paquet de GNU R à propos de classification
Maintainer: Dirk Eddelbuettel
Versions of package r-cran-class
ReleaseVersionArchitectures
stretch7.3-14-1amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
squeeze7.3-2-2amd64,armel,i386,ia64,kfreebsd-amd64,kfreebsd-i386,mips,mipsel,powerpc,s390,sparc
wheezy7.3-4-1amd64,armel,armhf,i386,ia64,kfreebsd-amd64,kfreebsd-i386,mips,mipsel,powerpc,s390,s390x,sparc
jessie7.3-11-1amd64,armel,armhf,i386
buster7.3-15-1amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
bullseye7.3-15-1amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
sid7.3-15-1amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
Debtags of package r-cran-class:
devellang:r
roleshared-lib
sciencecalculation, modelling
useanalysing
Popcon: 954 users (383 upd.)*
Versions and Archs
License: DFSG free
Git

Le paquet class fournit des fonctions et des ensembles de données pour prendre en charge le douzième chapitre à propos de « classification » du livre « Modern Applied Statistics with S » (4ème édition) de W.N. Venables et B.D. Ripley. L’URL suivant fournit davantage de détails sur le livre : http://www.stats.ox.ac.uk/pub/MASS4

R-cran-cluster
paquet GNU R pour l'analyse de grappe, écrit par Rousseeuw et al.
Maintainer: Dirk Eddelbuettel
Versions of package r-cran-cluster
ReleaseVersionArchitectures
sid2.1.0-2amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
squeeze1.13.1-1amd64,armel,i386,ia64,kfreebsd-amd64,kfreebsd-i386,mips,mipsel,powerpc,s390,sparc
wheezy1.14.2-1amd64,armel,armhf,i386,ia64,kfreebsd-amd64,kfreebsd-i386,mips,mipsel,powerpc,s390,s390x,sparc
jessie1.15.3-1amd64,armel,armhf,i386
stretch2.0.5-1amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
buster2.0.7-1-1amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
bullseye2.1.0-2amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
Debtags of package r-cran-cluster:
devellang:r, library
fieldstatistics
roleapp-data
suitegnu
Popcon: 762 users (421 upd.)*
Versions and Archs
License: DFSG free
Git

Ce paquet fournit les fonctions et les jeux de données pour l'analyse de grappe, écrit à l'origine par Peter Rousseeuw, Anja Struyf et Mia Hubert.

Ce paquet fait partie d'un ensemble de paquets recommandés par R et fournis avec les sources de R lui-même.

R-cran-gbm
GNU R package providing Generalized Boosted Regression Models
Versions of package r-cran-gbm
ReleaseVersionArchitectures
jessie2.1-1amd64,armel,armhf,i386
stretch2.1.1-1amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
buster2.1.5-1amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
sid2.1.5-1amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
bullseye2.1.5-1amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
Popcon: 23 users (7 upd.)*
Versions and Archs
License: DFSG free
Git

This package implements extensions to Freund and Schapire's AdaBoost algorithm and Friedman's gradient boosting machine. Includes regression methods for least squares, absolute loss, t-distribution loss, quantile regression, logistic, multinomial logistic, Poisson, Cox proportional hazards partial likelihood, AdaBoost exponential loss, Huberized hinge loss, and Learning to Rank measures (LambdaMart).

R-cran-mass
paquet GNU R pour MASS de Venables et Ripley
Maintainer: Dirk Eddelbuettel
Versions of package r-cran-mass
ReleaseVersionArchitectures
buster7.3-51.1-1amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
sid7.3-51.4-2amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
bullseye7.3-51.4-2amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
squeeze7.3-7-1amd64,armel,i386,ia64,kfreebsd-amd64,kfreebsd-i386,mips,mipsel,powerpc,s390,sparc
wheezy7.3-19-1amd64,armel,armhf,i386,ia64,kfreebsd-amd64,kfreebsd-i386,mips,mipsel,powerpc,s390,s390x,sparc
jessie7.3-34-1amd64,armel,armhf,i386
stretch7.3-45-1amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
Debtags of package r-cran-mass:
devellang:r
fieldstatistics
suitegnu
Popcon: 1049 users (505 upd.)*
Versions and Archs
License: DFSG free
Git

Le paquet MASS fournit des fonctions et ensembles de données pour prendre en charge le livre « Modern Applied Statistics with S » (quatrième édition) de W.N. Venables et B.D. Ripley. L’URL suivant fournit plus de détails à propos du livre : http://www.stats.ox.ac.uk/pub/MASS4

The package is enhanced by the following packages: r-cran-pscl
R-cran-mcmcpack
routines de R d’estimation de Monte-Carlo par chaînes de Markov
Versions of package r-cran-mcmcpack
ReleaseVersionArchitectures
wheezy1.2-3-1amd64,armel,armhf,i386,ia64,kfreebsd-amd64,kfreebsd-i386,mips,mipsel,powerpc,s390,s390x,sparc
sid1.4-5-1amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
bullseye1.4-5-1amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
buster1.4-4-1amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
squeeze1.0-7-1amd64,armel,i386,ia64,kfreebsd-amd64,kfreebsd-i386,mips,mipsel,powerpc,s390,sparc
jessie1.3-3-1amd64,armel,armhf,i386
stretch1.3-8-1amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
Debtags of package r-cran-mcmcpack:
devellang:r, library
fieldstatistics
roleapp-data
suitegnu
Popcon: 43 users (6 upd.)*
Versions and Archs
License: DFSG free
Git

Il s’agit d’un ensemble de routines pour GNU R qui met en œuvre divers modèles statistiques et économétriques d’estimation de Monte-Carlo par chaînes de Markov (MCMC), qui permettent de « résoudre » des modèles qui seraient autrement insolubles avec les techniques traditionnelles, en particulier les problèmes dans les statistiques (où un ou plusieurs « a priori » sont utilisés comme partie de la procédure, au lieu de l’hypothèse d’ignorance du « vrai » point estimé), quoique que MCMC peut aussi être utilisé pour résoudre des problèmes de statistiques fréquentistes avec des a priori non instructifs. Les techniques de MCMC sont aussi préférables à l’estimation directe en présence de données manquantes.

Sont actuellement implémentées plusieurs routines d’inférence écologique (EI) (pour l’estimation des attributs ou comportement au niveau individuel à partir de données agrégées, tels que les rapports électoraux ou les résultats de recensement), ainsi que des modèles de panel linéaire classique et des données transversales, quelques routines de visualisation pour des diagnostics d’EI, deux modèles de théories de réponse à l’item (ou estimation par point optimal — ideal-point estimation), l’analyse de facteurs métriques, ordinaux et de réponse mixte, et des modèles de régression gaussienne (linéaire) et de Poisson, de régression logistique (ou logit) et des modèles probit de réponses binaires ou ordinales.

Les paquets suggérés (r-cran-bayesm, -eco et -mnp) fournissent des modèles supplémentaires pouvant être utiles aux utilisateurs de ce paquet.

The package is enhanced by the following packages: r-cran-mcmc r-cran-mnp
R-cran-metrics
GNU R evaluation metrics for machine learning
Versions of package r-cran-metrics
ReleaseVersionArchitectures
buster0.1.4-1all
sid0.1.4-1all
bullseye0.1.4-1all
Popcon: 14 users (5 upd.)*
Versions and Archs
License: DFSG free
Git

An implementation of evaluation metrics in R that are commonly used in supervised machine learning. It implements metrics for regression, time series, binary classification, classification, and information retrieval problems. It has zero dependencies and a consistent, simple interface for all functions.

R-cran-mlbench
GNU R Machine Learning Benchmark Problems
Versions of package r-cran-mlbench
ReleaseVersionArchitectures
bullseye2.1-1-3amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
buster2.1-1-3amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
stretch2.1-1-1amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
sid2.1-1-3amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
Popcon: 45 users (14 upd.)*
Versions and Archs
License: DFSG free
Git

This GNU R package provices a collection of artificial and real-world machine learning benchmark problems, including, e.g., several data sets from the UCI repository.

R-cran-mlr
Machine learning in GNU R
Versions of package r-cran-mlr
ReleaseVersionArchitectures
sid2.16.0-1amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
buster2.13-1amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
bullseye2.16.0-1amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
Popcon: 15 users (15 upd.)*
Versions and Archs
License: DFSG free
Git

Interface to a large number of classification and regression techniques, including machine-readable parameter descriptions. There is also an experimental extension for survival analysis, clustering and general, example-specific cost-sensitive learning. Generic resampling, including cross-validation, bootstrapping and subsampling. Hyperparameter tuning with modern optimization techniques, for single- and multi-objective problems. Filter and wrapper methods for feature selection. Extension of basic learners with additional operations common in machine learning, also allowing for easy nested resampling. Most operations can be parallelized.

R-cran-mnp
paquet de GNU R pour ajuster des modèles probit multinomiaux (MNP)
Versions of package r-cran-mnp
ReleaseVersionArchitectures
buster3.1-0-2amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
bullseye3.1-0-2amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
sid3.1-0-2amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
stretch2.6-4-1amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
jessie2.6-4-1amd64,armel,armhf,i386
wheezy2.6-2-1amd64,armel,armhf,i386,ia64,kfreebsd-amd64,kfreebsd-i386,mips,mipsel,powerpc,s390,s390x,sparc
squeeze2.6-1-2amd64,armel,i386,ia64,kfreebsd-amd64,kfreebsd-i386,mips,mipsel,powerpc,s390,sparc
Debtags of package r-cran-mnp:
devellang:r, library
fieldstatistics
roleapp-data
suitegnu
Popcon: 33 users (3 upd.)*
Versions and Archs
License: DFSG free
Git

MNP est un paquet de R qui adapte des modèles probit multinomiaux bayésiens (MNP) à l’aide de méthodes de Monte-Carlo par chaînes de Markov (MCMC). Selon le modèle probit multinomial standard, il peut aussi adapter des modèles avec différents ensembles de choix pour chaque observation et trier complètement ou partiellement toutes les alternatives disponibles. L'estimation se fonde sur l'algorithme d'augmentation marginale efficace des données développé par Imai et van Dyk (2004).

R-cran-msm
modèles de Markov multi-état et caché en temps continu
Versions of package r-cran-msm
ReleaseVersionArchitectures
wheezy1.1-1amd64,armel,armhf,i386,ia64,kfreebsd-amd64,kfreebsd-i386,mips,mipsel,powerpc,s390,s390x,sparc
jessie1.4-2amd64,armel,armhf,i386
bullseye1.6.7-1amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
sid1.6.7-1amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
stretch1.6.4-1amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
buster1.6.6-2amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
squeeze0.9.7-1amd64,armel,i386,ia64,kfreebsd-amd64,kfreebsd-i386,mips,mipsel,powerpc,s390,sparc
Debtags of package r-cran-msm:
interfacecommandline
roleprogram
Popcon: 38 users (8 upd.)*
Versions and Archs
License: DFSG free
Git

Fonctions pour adapter des modèles de Markov temporels cachés et continus à des données longitudinales. Les taux de transitions de Markov et le processus de sortie de Markov caché peuvent être modélisés en terme de co- variables. Une variété de schémas d'observation sont permis, qui comprennent une surveillance des processus à des temps arbitraires, une observation complète des processus, et des états censurés.

Please cite: Christopher H. Jackson: Multi-State Models for Panel Data: The msm Package for R. Journal of Statistical Software 38(8):1-29 (2011)
R-cran-tgp
GNU R Bayesian treed Gaussian process models
Versions of package r-cran-tgp
ReleaseVersionArchitectures
bullseye2.4-14-4amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
jessie2.4-9-1amd64,armel,armhf,i386
stretch2.4-14-2amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
buster2.4-14-4amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
sid2.4-14-4amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
Popcon: 20 users (5 upd.)*
Versions and Archs
License: DFSG free
Git

Bayesian nonstationary, semiparametric nonlinear regression and design by treed Gaussian processes (GPs) with jumps to the limiting linear model (LLM). Special cases also implemented include Bayesian linear models, CART, treed linear models, stationary separable and isotropic GPs, and GP single-index models. Provides 1-d and 2-d plotting functions (with projection and slice capabilities) and tree drawing, designed for visualization of tgp-class output. Sensitivity analysis and multi-resolution models are supported. Sequential experimental design and adaptive sampling functions are also provided, including ALM, ALC, and expected improvement. The latter supports derivative-free optimization of noisy black-box functions.

Root-system
metapackage to install all ROOT packages
Versions of package root-system
ReleaseVersionArchitectures
wheezy5.34.00-2all
jessie5.34.19+dfsg-1.2all
Debtags of package root-system:
fieldphysics
Popcon: 0 users (0 upd.)*
Versions and Archs
License: DFSG free
Git

The ROOT system provides a set of OO frameworks with all the functionality needed to handle and analyze large amounts of data efficiently.

With the data defined as a set of objects, specialized storage methods can give direct access to the separate attributes of the selected objects, without having to touch the bulk of the data. Included are histogramming methods in 1, 2 and 3 dimensions, curve fitting, function evaluation, minimization, graphics and visualization classes to allow the easy creation of an analysis system that can query and process the data interactively or in batch mode.

The command language, the scripting (or macro) language, and the programming language are all C++, thanks to the built-in CINT C++ interpreter. This interpreter removes the time consuming compile/link cycle, allowing for fast prototyping of the macros, and providing a good environment to learn C++. If more performance is needed, the interactively developed macros can be compiled using a C++ compiler.

The system has been designed in such a way that it can query its databases in parallel on MPP machines or on clusters of workstations or high-end PCs. ROOT is an open system that can be dynamically extended by linking external libraries. This makes ROOT a premier platform on which to build data acquisition, simulation and data analysis systems.

This package is a metapackage to ensure the installation of all possible ROOT packages on a system.

Scilab-ann
Scilab module for artificial neural networks
Versions of package scilab-ann
ReleaseVersionArchitectures
jessie0.4.2.4-1all
stretch0.4.2.4-1all
squeeze0.4.2.3-3all
sid0.4.2.4-1all
wheezy0.4.2.4-1all
Debtags of package scilab-ann:
devellibrary
roledevel-lib, shared-lib
Popcon: 6 users (0 upd.)*
Versions and Archs
License: DFSG free
Svn

This module implements artificial neural networks capabilities into the Scilab language. Current features are:

  • Only layered feedforward networks are supported directly at the moment (for others use the "hooks" provided)
  • Unlimited number of layers
  • Unlimited number of neurons per each layer separately
  • User defined activation function (defaults to logistic)
  • User defined error function (defaults to SSE)
  • Algorithms implemented so far:
  • standard (vanilla) with or without bias, on-line or batch
  • momentum with or without bias, on-line or batch
  • SuperSAB with or without bias, on-line or batch
  • Conjugate gradients
  • Jacobian computation
  • Computation of result of multiplication between "vector" and Hessian
  • Some helper functions provided
Torch-core-free
Scientific Computing Framework For Luajit (Core Components)
Versions of package torch-core-free
ReleaseVersionArchitectures
buster20171127amd64,armel,armhf,ppc64el
Popcon: 0 users (0 upd.)*
Versions and Archs
License: DFSG free
Git

Torch is a scientific computing framework with wide support for machine learning algorithms that puts GPUs first. It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation.

A summary of core features:

  • a powerful N-dimensional array
  • lots of routines for indexing, slicing, transposing, ...
  • amazing interface to C, via LuaJIT
  • linear algebra routines
  • neural network, and energy-based models
  • numeric optimization routines
  • Fast and efficient GPU support
  • Embeddable, with ports to iOS, Android and FPGA backends

The goal of Torch is to have maximum flexibility and speed in building your scientific algorithms while making the process extremely simple. Torch comes with a large ecosystem of community-driven packages in machine learning, computer vision, signal processing, parallel processing, image, video, audio and networking among others, and builds on top of the Lua community.

At the heart of Torch are the popular neural network and optimization libraries which are simple to use, while having maximum flexibility in implementing complex neural network topologies. You can build arbitrary graphs of neural networks, and parallelize them over CPUs and GPUs in an efficient manner.

This package is a metapackage, which pulls the following core and free modules for you: cwrap, paths, sys, xlua, torch7, nn, graph, nngraph, optim, sundown, dok, trepl, image.

Note that cutorch (CUDA backend for torch) and cunn (CUDA backend for neural network) are not present in this metapacakge - they will be shipped in the torch-core-contrib metapackage in the future.

Toulbar2
Exact combinatorial optimization for Graphical Models
Versions of package toulbar2
ReleaseVersionArchitectures
bullseye1.0.0+dfsg3-2amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
sid1.0.0+dfsg3-2amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
buster1.0.0+dfsg3-2amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
Popcon: 7 users (4 upd.)*
Versions and Archs
License: DFSG free
Git

Toulbar2 is an exact discrete optimization tool for Graphical Models such as Cost Function Networks, Markov Random Fields, Weighted Constraint Satisfaction Problems and Bayesian Nets.

Vowpal-wabbit
algorithme rapide et extensible d’apprentissage automatique en ligne
Maintainer: Yaroslav Halchenko
Versions of package vowpal-wabbit
ReleaseVersionArchitectures
jessie7.3-1.1amd64,armel,armhf,i386
wheezy6.1-1amd64,armel,armhf,i386,ia64,kfreebsd-amd64,kfreebsd-i386,mips,mipsel,powerpc,s390,s390x,sparc
sid8.6.1.dfsg1-1amd64,i386
sid7.3-1.1arm64,armel,armhf,mips64el,mipsel,ppc64el,s390x
squeeze4.1+20100420-1amd64,armel,i386,ia64,kfreebsd-amd64,kfreebsd-i386,mips,powerpc,s390
upstream8.8.0
Debtags of package vowpal-wabbit:
interfacecommandline
roleprogram
scopeutility
Popcon: 3 users (0 upd.)*
Newer upstream!
License: DFSG free
Git

Vowpal Wabbit est un algorithme rapide d’apprentissage automatique en ligne. VW est un « algorithme de la plus forte pente » (gradient descent) sur une « fonction économique » (loss fonction, plusieurs sont disponibles).

 Fonctions de VW :
 – spécification de données d’entrée flexible ;
 – apprentissage rapide ;
 – extensibilité (empreinte mémoire délimitée, convenable pour le calcul
   distribué) ;
 – appariement de fonctions.
Screenshots of package vowpal-wabbit
Weka
algorithmes d'apprentissage automatique pour l’exploration de données
Versions of package weka
ReleaseVersionArchitectures
buster3.6.14-1all
wheezy3.6.6-1all
stretch3.6.14-1all
squeeze3.6.0-3all
jessie3.6.11-1all
sid3.6.14-1all
bullseye3.6.14-1all
upstream3.8.3
Debtags of package weka:
fieldstatistics
interfacecommandline, x11
roleprogram
sciencecalculation
scopeutility
useanalysing, calculating
works-withdb, text
x11application
Popcon: 49 users (26 upd.)*
Newer upstream!
License: DFSG free
Git

Weka est un ensemble d’algorithmes d'apprentissage automatique écrits en Java pouvant être utilisés soit à partir de la ligne de commande, ou appelés à partir d’une ligne de code Java. Weka est parfaitement adapté au développement de nouveaux projets d’apprentissage automatique.

Les programmes mis en œuvre couvrent les domaines d’induction d’arbre de décision, d’apprentissage de règles d’association, de générateurs d’arbre de modèles, de machines à vecteurs de support, de régression avec pondération locale, d’apprentissage basé sur la mémoire, « bagging », « boosting » et « stacking ». Des méthodes pour grappes sont aussi incluses ainsi qu’un apprentissage de règles d’association. En plus des schémas d’apprentissage à proprement parler, Weka fournit aussi une grande variété d’outils pouvant être utilisés pour le prétraitement d’ensembles de données.

Ce paquet fournit les exécutables et des exemples.

Other screenshots of package weka
VersionURL
3.6.6-1https://screenshots.debian.net/screenshots/000/010/528/large.png
Screenshots of package weka
Yap
système Prolog de haute performance
Maintainer: Ralf Treinen
Versions of package yap
ReleaseVersionArchitectures
stretch6.2.2-6amd64,arm64,armel,armhf,i386
squeeze5.1.3-4amd64,armel,i386,powerpc,s390
wheezy5.1.3-6amd64,armel,armhf,i386,powerpc,s390
jessie6.2.2-2amd64,armel,armhf,i386
sid6.2.2-6amd64,arm64,armel,armhf,i386
Debtags of package yap:
develcompiler, interpreter, lang:prolog
roleprogram
Popcon: 15 users (0 upd.)*
Versions and Archs
License: DFSG free
Git

Il s’agit d’un compilateur haute performance pour Prolog, développé à LIACC, Université de Porto et à COPPE Sistemas, UFRJ. Le moteur Prolog de YAP est basé sur la « Machine abstraite de Warren », avec plusieurs optimisations pour améliorer la performance. YAP suit le standard d’Édimbourg et est largement compatible avec le standard ISO-Prolog et avec Quintus Prolog et SICStus Prolog.

YAP fournit un solveur de contraintes sur les nombres réels et prend en charge le langage de programmation par contraintes CHR (Constraint Handling Rules).

Official Debian packages with lower relevance

Ask
kit d'échantillonage pour grands espaces expérimentaux
Versions of package ask
ReleaseVersionArchitectures
jessie1.0.1-2all
sid1.1.1-3all
bullseye1.1.1-3all
buster1.1.1-3all
stretch1.1.1-1all
Popcon: 1 users (0 upd.)*
Versions and Archs
License: DFSG free
Git

Le kit d’échantillonnage adaptatif (ASK) est un ensemble d'outils pour échantillonner de grands espaces expérimentaux. Lorsque l'espace est petit, la réponse peut être mesurée pour chaque point de l'espace. Lorsque l'espace est grand, la réalisation d'une mesure exhaustive n'est pas possible soit en terme de temps d'exécution, soit de faisabilité. ASK essaye de trouver de bonnes approximations de la réponse en échantillonnant seulement une petite portion de l'espace. ASK dispose de multiples algorithmes d'apprentissage actif pour prioriser l'exploration des parties intéressantes de l'espace expérimental.

Other screenshots of package ask
VersionURL
1.1.1-1https://screenshots.debian.net/screenshots/000/015/157/large.png
Screenshots of package ask
Libacovea-dev
library for analyzing compiler options via evolutionary algorithms
Maintainer: Al Stone
Versions of package libacovea-dev
ReleaseVersionArchitectures
squeeze5.1.1-2amd64,armel,i386,ia64,kfreebsd-amd64,kfreebsd-i386,mips,mipsel,powerpc,s390,sparc
Debtags of package libacovea-dev:
roledevel-lib
Popcon: 0 users (0 upd.)*
Versions and Archs
License: DFSG free

The ACOVEA (Analysis of Compiler Options via Evolutionary Algorithm) library that implements a genetic algorithm to find the "best" options for compiling programs with the GNU Compiler Collection (GCC) C and C++ compilers. "Best," in this context, is defined as those options that produce the fastest executable program from a given source code.

libacovea is part of a C++ framework that can be extended to test other programming languages and non-GCC compilers.

This package contains the development files for libacovea.

Libdlib-dev
C++ toolkit for machine learning and computer vision - development
Versions of package libdlib-dev
ReleaseVersionArchitectures
buster19.10-3amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
sid19.10-3amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
stretch18.18-2amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
bullseye19.10-3amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
upstream19.18
Popcon: 6 users (2 upd.)*
Newer upstream!
License: DFSG free
Git

Dlib is a general purpose cross-platform open source software library written in the C++ programming language. It now contains software components for dealing with networking, threads, graphical interfaces, complex data structures, linear algebra, statistical machine learning, image processing, data mining, XML and text parsing, numerical optimization, Bayesian networks, and numerous other tasks.

This package contains the development headers.

Libfclib-dev
read and write problems from the Friction Contact Library (headers)
Versions of package libfclib-dev
ReleaseVersionArchitectures
buster3.0.0+dfsg-2amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
sid3.0.0+dfsg-2amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
bullseye3.0.0+dfsg-2amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
Popcon: 1 users (0 upd.)*
Versions and Archs
License: DFSG free
Git

fclib is an open source collection of Frictional Contact (FC) problems stored in a specific HDF5 format, and an open source light implementation of Input/Output functions in C Language to read and write problems.

The goal of this work is to set up a collection of 2D and 3D Frictional Contact (FC) problems in order to set up a list of benchmarks; provide a standard framework for testing available and new algorithms; and share common formulations of problems in order to exchange data.

Fclib is an open-source scientific software primarily targeted at modeling and simulating nonsmooth dynamical systems

This package includes the libfclib development headers.

Libmkldnn-dev
Intel Math Kernel Library for Deep Neural Networks (dev)
Versions of package libmkldnn-dev
ReleaseVersionArchitectures
buster0.17.4-1amd64
sid1.1.1-1amd64
bullseye1.1.1-1amd64
Popcon: 1 users (3 upd.)*
Versions and Archs
License: DFSG free
Git

Intel(R) Math Kernel Library for Deep Neural Networks (Intel(R) MKL-DNN) is an open source performance library for deep learning applications. The library accelerates deep learning applications and framework on Intel(R) architecture. Intel(R) MKL-DNN contains vectorized and threaded building blocks which you can use to implement deep neural networks (DNN) with C and C++ interfaces.

DNN functionality optimized for Intel architecture is also included in Intel(R) Math Kernel Library (Intel(R) MKL). API in this implementation is not compatible with Intel MKL-DNN and does not include certain new and experimental features.

This package contains the header files, and symbol links to the shared object.

Libxsmm-dev
Matrix operations and deep learning primitives (development files)
Versions of package libxsmm-dev
ReleaseVersionArchitectures
sid1.9-1amd64
Popcon: 0 users (0 upd.)*
Versions and Archs
License: DFSG free
Git

LIBXSMM is a library targeting Intel Architecture for specialized dense and sparse matrix operations, and deep learning primitives.

This package contains the static libraries and header files.

Python3-hdmedians
high-dimensional medians in Python3
Versions of package python3-hdmedians
ReleaseVersionArchitectures
sid0.13~git20171027.8e0e9e3-1amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
buster0.13~git20171027.8e0e9e3-1amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
bullseye0.13~git20171027.8e0e9e3-1amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
Popcon: 1 users (8 upd.)*
Versions and Archs
License: DFSG free
Git

Various definitions for a high-dimensional median exist and this Python package provides a number of fast implementations of these definitions. Medians are extremely useful due to their high breakdown point (up to 50% contamination) and have a number of nice applications in machine learning, computer vision, and high-dimensional statistics.

This package currently has implementations of medoid and geometric median with support for missing data using NaN.

Science-numericalcomputation
paquets pour le calcul numérique de Debian Science
Versions of package science-numericalcomputation
ReleaseVersionArchitectures
bullseye1.11all
sid1.11all
squeeze0.12all
jessie1.4all
stretch1.7all
buster1.10all
wheezy1.0all
Debtags of package science-numericalcomputation:
devellang:lisp
rolemetapackage, shared-lib
Popcon: 14 users (2 upd.)*
Versions and Archs
License: DFSG free
Git

Ce métapaquet installe les paquets de Debian Science concernant le calcul numérique. Les paquets fournissent un système de calcul et visualisation orienté matrice pour le calcul scientifique et l’analyse de données. Ces paquets sont similaires aux systèmes commerciaux tels que Matlab et IDL.

Science-statistics
paquets pour les statistiques de Debian Science
Versions of package science-statistics
ReleaseVersionArchitectures
jessie1.4all
sid1.11all
stretch1.7all
squeeze0.12all
wheezy1.0all
bullseye1.11all
buster1.10all
Debtags of package science-statistics:
rolemetapackage
suitedebian
Popcon: 15 users (3 upd.)*
Versions and Archs
License: DFSG free
Git

Ce paquet fait partie du mélange exclusif « Debian Science » et installe les paquets concernant les statistiques. Cette tâche est une tâche générale pouvant être utile pour n’importe quels travaux scientifiques. Elle dépend de beaucoup de paquets R ainsi que d’autres outils utiles pour faire des statistiques. De plus la tâche « Science Mathematics » est suggérée pour installer facultativement tous les logiciels relatifs aux mathématiques.

Science-typesetting
paquets pour la composition typographique de Debian Science
Versions of package science-typesetting
ReleaseVersionArchitectures
buster1.10all
squeeze0.12all
wheezy1.0all
jessie1.4all
stretch1.7all
bullseye1.11all
sid1.11all
Debtags of package science-typesetting:
rolemetapackage
suitedebian
Popcon: 8 users (2 upd.)*
Versions and Archs
License: DFSG free
Git

Ce métapaquet installe les paquets de Debian Science concernant la composition typographique. L’utilisateur peut aussi être intéressé par field::physics de debtag.

Debian packages in contrib or non-free

Caffe-cuda
Fast, open framework for Deep Learning (Meta)
Versions of package caffe-cuda
ReleaseVersionArchitectures
stretch1.0.0~rc4-1 (contrib)amd64
buster1.0.0+git20180821.99bd997-2 (contrib)amd64
Popcon: 0 users (0 upd.)*
Versions and Archs
License: DFSG free, but needs non-free components
Git

Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by the Berkeley AI Research Lab (BAIR) and community contributors.

This metapackage pulls CUDA version of caffe:

  • caffe-tools-cuda
  • libcaffe-cuda*
  • python3-caffe-cuda And suggests these packages:

  • libcaffe-cuda-dev

  • caffe-doc

Note, this CUDA version cannot co-exist with the CPU_ONLY version.

Please cite: Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama and Trevor Darrell: Caffe: Convolutional Architecture for Fast Feature Embedding. (eprint) arXiv preprint arXiv:1408.5093 (2014)

Packaging has started and developers might try the packaging code in VCS

Spacy
Industrial-strength Natural Language Processing (NLP)
Versions of package spacy
ReleaseVersionArchitectures
VCS2.0.17-1all
Versions and Archs
License: MIT
Debian package not available
Git
Version: 2.0.17-1

spaCy is a library for advanced Natural Language Processing in Python and Cython. It’s built on the very latest research, and was designed from day one to be used in real products. spaCy comes with pre-trained statistical models and word vectors, and currently supports tokenization for 30+ languages. It features the fastest syntactic parser in the world, convolutional neural network models for tagging, parsing and named entity recognition and easy deep learning integration.

Unofficial packages built by somebody else

Python3-orange
Data mining framework
Responsible: Mitar
License: GPLv3

Orange is a component-based data mining software. It includes a range of data visualization, exploration, preprocessing and modeling techniques. It can be used through a nice and intuitive user interface or, for more advanced users, as a module for Python programming language.

No known packages available but some record of interest (WNPP bug)

Flann - wnpp
Fast Library for Approximate Nearest Neighbors
License: BSD
Debian package not available
Language: C++

FLANN is a library for performing fast approximate nearest neighbor searches in high dimensional spaces. It contains a collection of algorithms we found to work best for nearest neighbor search and a system for automatically choosing the best algorithm and optimum parameters depending on the dataset.

Pybrain - wnpp
Modular Machine Learning Library
License: BSD
Debian package not available
Language: Python

PyBrain is a modular machine learning library for Python. Its goal is to offer flexible, easy-to-use yet still powerful algorithms for Machine Learning Tasks and a variety of predefined environments to test and compare your algorithms.

PyBrain currently features algorithms for Supervised Learning, Unsupervised Learning, Reinforcment Learning and Black-box Optimization.

*Popularitycontest results: number of people who use this package regularly (number of people who upgraded this package recently) out of 202987