Debian Science Project
Summary
Machine Learning
Debian Science - Machine Learning-pakker

Denne metapakke vil installere pakker nyttige for maskinlæring. Inkluderede pakkeinterval går fra videnbaseret (ekspert) følgeslutningssystemer til programimplementeringer for avanceret statistik, som dominerer nutidens metoder.

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
Automatisk klassificering eller clustering
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AutoClass løser problemet med automatisk opdagelse af klasser i data (også kaldet clustering eller uovervåget indlæring) i modsætning til generering af klassebeskrivelser fra mærkede eksempler (kaldet overvåget indlæring). Det sigter på at opdage »naturlige« klasser i dataene. AutoClass kan bruges på observationer af ting, der kan beskrives ved et antal egenskaber uden at referere til andre ting. De dataværdier, der svarer til hver egenskab er begrænset til enten at være numre eller elementer i et fast symbolsæt. For numeriske data skal man angive målefejlen.

caffe-cpu
Hurtig, åben ramme for Deep Learning - metapakke
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Caffe er en dyb læringsramme lavet med omtanke for udtryk, hastighed og modulopbygning. Programmet er udviklet af Berkeley AI Research Lab (BAIR) og bidragsydere fra fællesskabet.

Denne metapakke henter CPU_ONLY-versionen af caffe: * caffe-tools-cpu

  • libcaffe-cpu*
  • python3-caffe-cpu Og foreslår disse pakker:

  • libcaffe-cpu-dev

  • caffe-doc

Bemærk denne CPU_ONLY-version kan ikke sameksistere med CUDA-versionen.

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-kompiler
Maintainer: Salvador Abreu
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GNU Prolog er en fri Prologkompiler med begrænsningsløsning over finitte domæner (FD). GNU Prolog overholder hovedsagelig ISO-standarden og er en del af Prolog Commons-initiativet.

Denne pakke indeholder kompileren og kørselstidssystemet for ISO- standardversionen for GNU Prolog, inklusive implementeringen af prototypemodulet.

libcv-dev
??? missing short description for package libcv-dev :-(
Maintainer: Debian Science Team
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Please cite: Gary Bradski and Adrian Kaehler: Learning OpenCV: Computer Vision with the OpenCV Library (2008)
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libevocosm-dev
C++ framework for developing evolutionary algorithms
Maintainer: Al Stone (Chris Lamb)
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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
Udviklingsbiblioteker og teksthovedfiler for FANN
Maintainer: Christian Kastner
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Fast Artificial Neural Network Library er et frit neuralt netværksbibliotek, udviklet i åben kildekode, som implementerer flerlags neurale netværk med kunstig intelligens i C og med understøttelse for både fuldt forbundne og delvist forbundne netværk. Kørsel på flere platforme i både faste og flydende heltal er understøttet. Biblioteket inkluderer en ramme for nem håndtering af træningsdatasæt. Det er nemt at bruge, alsidigt, veldokumenteret og hurtigt.

Denne pakke indeholder teksthovedfilerne og statiske biblioteker, som er krævet for at udvikle libfann-programmer.

libga-dev
C++ Library of Genetic Algorithm Components
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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
Udviklingsbiblioteker og hovedfiler for LIBLINEAR
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LIBLINEAR er et bibliotek for læring af lineære klassifikatorer for omfangsrige programmer. Biblioteket understøtter Support Vector Machines (SVM) med L2-og L1-tab. Logistisk regression, flerklasseklassifikation og også Linear Programming Machines (L1-regulerede SVM'er). Dets beregningsmæssige kompleksitet skalerer lineært med antallet af træningseksempler, hvilket gør det til et af de hurtigste SVM-løsere.

Denne pakke indeholder hovedfilerne og de statiske biblioteker.

libmlpack-dev
intuitive, fast, scalable C++ machine learning library (development libs)
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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
Udviklingsbiblioteker og teksthovedfiler for LIBOCAS
Maintainer: Christian Kastner
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Dette bibliotek implementerer Optimized Cutting Plane Algorithm (OCAS) til træning af lineære Support Vector Machine-klassifikatører (SVM) fra store mængder data. Beregningsindsatsen for OCAS skalerer lineært med antallet af træningseksempler. Det er en af de hurtigste SVM-løsere til løsning af lineære og multiklasse L2-legaliserede SVM'er.

Denne pakke indeholder hovedfilerne og de statiske biblioteker.

libroot-math-mlp-dev
Flerlags perceptron-udvidelse for ROOT - udviklingsfiler
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ROOT-systemet tilbyder et sæt af OO-rammeværktøjer med al den funktionalitet, som er krævet for at håndtere og analysere store mængder af data effektivt.

Denne pakke indeholder udviklingsfiler til mlp-udvidelsesmodulet for ROOT, der tilbyder en Multi Layer Perceptron Neural Network-pakke for ROOT.

libroot-montecarlo-vmc-dev
Virtuel Monte-Carlo-bibliotek for ROOT - udviklingsfiler
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ROOT-systemet tilbyder et sæt af OO-rammeværktøjer med al den funktionalitet, som er krævet for at håndtere og analysere store mængder af data effektivt.

Denne pakke indeholder udviklingsfiler for Vmc-biblioteket for ROOT.

libroot-tmva-dev
Toolkit for multivariate data analysis - development files
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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
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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: Luca Denti, Yuri Pirola, Marco Previtali, Tamara Ceccato, Gianluca Della Vedova, Raffaella Rizzi and Paola Bonizzoni: Shark: fishing relevant reads in an RNA-Seq sample. (eprint) Bioinformatics 37(4):464-472 (2020)
Registry entries: Bio.tools  Bioconda 
libshogun-dev
Large Scale Machine Learning Toolbox
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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
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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
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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
Udviklingsfiler for C++-computervisionsbiblioteket
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Vision med generiske algoritmer (VIGRA) er et computervisionbibliotek, som placerer sit hovedfokus på fleksible algoritmer, da algoritmerne repræsenterer den grundlægende viden indenfor dette felt. Biblioteket blev som konsekvent bygget med brug af generisk programmering som introduceret af Stepanov og Musser og eksemplicificeret i C++ Standard Template Library. Ved at skrive nogle få adaptere (billediteratorer og accessorer) kan du bruge VIGRA's algoritmer oven på dine datastrukturer, inden i dit miljø.

Denne pakke indeholder teksthovederne og udviklingsfilerne krævet for at bygge programmer og pakker med brug af VIGRA.

lua-torch-graph
Graph Computation Package for Torch Framework
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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
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"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
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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
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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
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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
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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
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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
Markov Cluster-algoritmen
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Pakken MCL er en implementering af MCL-algoritmen, og tilbyder redskaber til manipulation af matricer med spredning (på engelsk »sparse matrices«, den essentielle datastruktur i MCL-algoritmen) og til at foretage eksperimenter med klynger.

MCL anvendes på nuværende tidspunkt i videnskaber som biologi (detektering af proteinfamilier, arvemasseforskning), datalogi (knudepunktklynger i »vært til vært«-netværk) og lingvistik (tekstanalyse).

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)
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mrgingham
Chessboard finder for visual calibration routines
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Given an observed image containing a chessboard or a grid of circles, mrgingham locates the board in the image, and precisely computes the location of the chessboard corners (or circle centers). This is similar to the routines in OpenCV, but is faster and more robust.

This package provides the user-facing tools

octave-ga
Genetisk optimeringskode for Octave
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Denne pakke tilbyder funktionalitet til at arbejde med genetiske algoritmer i Octave, et numerisk beregningsprogram. Pakken tilbyder ga()- funktionen, som fungerer på samme måder for andre optimeringsfunktioner i Octave.

Denne Octave-udvidelsespakke er en del af projektet Octave-Forge.

pgapack
Genetisk algoritmepakke til mange formål
Maintainer: Dirk Eddelbuettel
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PGAPack er en data-struktur-neutral, parallel genetisk algoritmepakke til mange formål, som bliver udviklet på Argonne National Laboratory.

Denne pakke indeholder hovedfiler, manualsider, eksempler og test. For at bruge pgpack skal du installere enten pakken libpgapack-serial (»en cpu«) eller pakken libpgapack-mpi (»parallel«).

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python3-amp
Atomistic Machine-learning Package (python 3)
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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-fann2
Python 3-bindinger for FANN
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Fast Artificial Neural Network Library er et frit neuralt netværksbibliotek, udviklet i åben kildekode, som implementerer flerlags neurale netværk med kunstig intelligens i C og med understøttelse for både fuldt forbundne og delvist forbundne netværk.

Denne pakke indeholder Python 3-bindingerne for FANN.

python3-genetic
genetic algorithms in Python
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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
Dyb læringsramme der afvikles på Theano eller TensorFlow
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Keras er et Pythonbibliotek til maskinlæring baseret på dybe (flerlags) kunstige neurale netværk (DNN), som følger et minimalistisk og modulært design med fokus på hurtig eksperimentering.

Funktioner for DNN'er såsom neurale lag, omkostningsfunktioner, optimeringsprogrammer, opstartsskemaer, aktiveringsfunktioner og reguleringsskemaer er tilgængelige i Keras som uafhængige moduler, som kan samles efter behov til at oprette sekvensmodeller eller mere komplekse arkitekturer. Keras understøtter »convolutions neural networks« (CNN, brugt til billedgenkendelse respektiv klassifikation) og »recurrent neural networks« (RNN, egnet for sekvensanalyse som i naturlig sprogbehandling).

Biblioteket afvikles som et abstraktionslag oven på Theano (kompiler til matematikudtryk) som standard, hvilket gør det muligt at accelerere beregningerne ved at bruge (GP)GPU-enheder. Alternativt kan Keras afvikles på Googles TensorFlow (endnu ikke tilgængelig i Debian).

python3-lasagne
deep learning library build on the top of Theano (Python3 modules)
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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
Modulært værktøjssæt for databehandling
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Databehandlingsramme til Python for bygning af komplekse databehandlingsprogrammer ved at kombinere udbredte algoritmer for maskinlæring til datakanaler og netværk. Implementerede algoritmer inkluderer: 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) og gaussiske klassifikationer.

The package is enhanced by the following packages: python3-sklearn
python3-mlpy
high-performance Python package for predictive modeling
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mlpy provides high level procedures that support, with few lines of code, the design of rich Data Analysis Protocols (DAPs) for preprocessing, clustering, predictive classification and feature selection. Methods are available for feature weighting and ranking, data resampling, error evaluation and experiment landscaping.

mlpy includes: SVM (Support Vector Machine), KNN (K Nearest Neighbor), FDA, SRDA, PDA, DLDA (Fisher, Spectral Regression, Penalized, Diagonal Linear Discriminant Analysis) for classification and feature weighting, I-RELIEF, DWT and FSSun for feature weighting, RFE (Recursive Feature Elimination) and RFS (Recursive Forward Selection) for feature ranking, DWT, UWT, CWT (Discrete, Undecimated, Continuous Wavelet Transform), KNN imputing, DTW (Dynamic Time Warping), Hierarchical Clustering, k-medoids, Resampling Methods, Metric Functions, Canberra indicators.

python3-opencv
Python 3-bindinger for computer vision-biblioteket
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Denne pakke indeholder Python 3-bindinger for OpenCV-biblioteket (Open Computer Vision).

Open Computer Vision Library er en samling af algoritmer og eksempelkode for diverse computer vision-problemer. Biblioteket er kompatibelt med IPL (Intels Image Processing Library) og, hvis tilgængeligt, kan det bruger IPP (Intels Integrated Performance Primitives) for bedre ydelse.

OpenCV tilbyder datatyper og operatorer på lavt niveau, der kan flyttes samt et sæt af funktioner på højt niveau for videooptagelse, billedbehandling og analyse, strukturel analyse, bevægelsesanalyse og objektsporing, objektgenkendelse, kamerakalibrering og 3D-rekonstruktion.

Please cite: Gary Bradski and Adrian Kaehler: Learning OpenCV: Computer Vision with the OpenCV Library (2008)
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python3-sklearn
Pythonmoduler for maskinlæring og dataundersøgelse - Python 3
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Scikit-learn er en samling af Pythonmoduler, der er relevante for maskin/statistisk læring og dataundersøgelse. En ikke udtømmende liste over inkluderet funktionalitet:

  • Gaussianske blandede modeller
  • Manifold-læring
  • kNN
  • SVM (via LIBSVM)

Denne pakke indeholder Python 3-versionen.

The package is enhanced by the following packages: python3-sklearn-pandas
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python3-statsmodels
Python 3-modul for estimering af statistiske modeller
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Statsmodels Python 3-modul tilbyder klasser og funktioner for estimering af flere kategorier af statistiske modeller. Disse inkluderer i øjeblikket lineære regressionsmodeller, OLS, GLS, WLS og GLS med AR(p)-fejl, generaliserede lineære modeller for flere distributionsfamilier og M-estimatorer for robuste lineære modeller. En omfattende liste over resultatstatistik er tilgængelig for hvert estimeringsproblem.

Please cite: Skipper Seabold and Josef Perktold: Statsmodels: Econometric and statistical modeling with python (eprint) (2010)
python3-thinc
Praktisk maskinlæring for NLP i Python
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Thinc er et maskinlæringsbibliotek, der driver spaCy https://spacy.io. Det har en kamptestet lineær model designet for store tynde læringsproblemer og en fleksibel neutral netværksmodel under udvikling for spaCy version 2.0 https://spacy.io/usage/v2.

Thinc er et praktisk værktøjssæt, der implementerer modeller, der følger arkitekturen »mbed, encode, attend, predict«. Sættet er designet til at være nemt at installere, effektiv i form af cpu-forbrug og optimeret for NLP og dyb læring med tekst - specielt, hierarkisk struktureret tekst og sekvenser med variabel længde.

python3-torch
Tensorer og dynamiske neurale netværk i Python - Pythongrænseflade
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PyTorch er en Pythonpakke, der tilbyder to funktioner på højt niveau.

(1) Tensorberegning (som NumPy) med stærk GPU-acceleration (2) Dybe neurale netværk bygget på et båndbaseret autograd-system

Du kan genbruge dine favoritpakker fra Python såsom NumPy, SciPy og Cython for at udvide PyTorch efter behov.

Dette er versionen kun for cpu af PyTorch (Pythongrænseflade).

Please cite: Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai and Soumith Chintala:
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python3-torch-sparse
PyTorch Extension-biblitoek for Optimized Autograd Sparse Matrix-operationer
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Denne pakke består af et lille udvidelsesbibliotek for optimerede rumlige matrix-operationer med autograd-understøttelse.

Denne pakke installerer biblioteket for Python 3.

python3-vigra
Python 3-bindinger for C++ computer vision-biblioteket
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Vision med generiske algoritmer (VIGRA) er et computervisionbibliotek, som placerer sit hovedfokus på fleksible algoritmer, da algoritmerne repræsenterer den grundlægende viden indenfor dette felt. Biblioteket blev som konsekvent bygget med brug af generisk programmering som introduceret af Stepanov og Musser og eksemplicificeret i C++ Standard Template Library. Ved at skrive nogle få adaptere (billediteratorer og accessorer) kan du bruge VIGRA's algoritmer oven på dine datastrukturer, inden i dit miljø.

Denne pakke eksporterer funktionaliteten for VIGRA-biblioteket til Python 3.

r-cran-amore
GNU R: A MORE flexible neural network package
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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
GNU R-pakke til bayesiansk statistik
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Pakken bayesm dækker mange vigtige modeller, der anvendes i markedsføring og mikro-økonometri applikationer. Pakken indeholder:

  • Bayes-regression (univariate eller multivariate dep var)
  • Multinomial Logit (MNL) og Multinomial Probit (MNP)
  • Multivariate Probit,
  • Multivariate blandinger af Normals
  • Hierarkisk lineære modeller med normale priors og kovariater
  • Hierarkisk Multinomial Logits med blanding af normale priors og kovariater
  • Bayesiansk analyse af valg-baserede conjoint data
  • Bayesian behandling af lineære instrumentale variable modeller
  • Analyis af Multivariate Ordinal-undersøgelsesdata med skalaforbrugs- heterogenitet (som i Rossi et al, JASA (01)).

For yderligere reference, se forfatternes bog, Bayesian Statistics And Marketing af Allenby, McCulloch og Rossi.

r-cran-class
GNU R-pakke for klassifikation
Maintainer: Dirk Eddelbuettel
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Pakken class tilbyder funktioner og datasæt til at understøtte kapitel 12 om »Klassifikation« i bogen »Modern Applied Statistics with S« (4. udgave) af W.N. Veneables og B.D. Ripley. Den følgende adresse tilbyder yderligere detaljer om bogen: http://www.stats.ox.ac.uk/pub/MASS4

r-cran-cluster
GNU R-pakke for klyngeanalyse af Rousseeuw et al
Maintainer: Dirk Eddelbuettel
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Denne pakke tilbyder funktioner og datasæt for klyngeanalyse oprindelig skrevet af Peter Rousseeuw, Anja Struyf og Mia Hubert.

Denne pakke er del af et sæt af pakker, som »anbefales« af R Core, og leveres med kildeudgivelser via opstrøm af R.

r-cran-gbm
GNU R-pakke der tilbyder Generalized Boosted Regression Models
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Denne pakke implementerer udvidelser til Freund og Schapires AdaBoost-algoritme og Friedmans gradientforstærkende maskine. Omfatter som minimum regressionsmetoder for mindste kvadrater, absolut tab, t-fordelingstab, kvantilregression, logistisk, multinomial logistik, Poisson, Cox-proportional fare med delvis sandsynlighed, AdaBoost-eksponentielt tab, Huberiseret hinge-tab og læring af at bedømme målinger (LambdaMart).

r-cran-mass
GNU R-pakke med Venables og Ripleys MASS
Maintainer: Dirk Eddelbuettel
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Pakken MASS tilbyder funktioner og datasæt som understøtter bogen »Modern Applied Statistics with S« (4. udgave) af W. N. Venables og B. D. Ripley. Den følgende adresse oplyser flere detaljer om bogen: Adresse: http://www.stats.ox.ac.uk/pub/MASS4

The package is enhanced by the following packages: r-cran-pscl
r-cran-mcmcpack
R-rutiner for Markov-kæde Monte Carlo-modelestimering
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Dette er et sæt af rutiner for GNU R, som implementerer diverse statistiske og økonometriske modeller, der bruger Markov-kæde Monte Carlo-estimering (MCMC), som giver mulighed for at »løse« modeller, som ellers ville være umedgørlige med traditionelle teknikker, specielt problemer indenfor bayesiansk statistik (hvor en eller flere »priors« bruges som en del af estimeringsproceduren, i stedet for en antagelse om uvidenhed om »sande« punktskøn), skønt MCMC også kan anvendes til at løse statistiske problemer indenfor hyppighed med uinformative »priors«. MCMC-teknikker foretrækkes også over direkte estimering, når der er manglende data.

I øjeblikket er et antal økologiske inferensrutiner (EI) implementeret (for estimering af attributter eller opførsel på individniveau fra opsamlede data, såsom valgte afkast eller optællingsresultater) samt som modeller for traditionelle lineære panel- og tværsnitsdata, nogle visualiseringsrutiner for EI-diagnostik, to post-response (eller ideal-punkts estimering) teorimodeller, metrisk, ordenstal, og blandet respons faktoranalyse, og modeller for Gaussian- (lineær) og Poisson-regression, logistisk regression (eller logit), og binære og ordinal-svar probit-modeller.

De foreslåede pakker (r-cran-bayesm, -eco og -mnp) indeholder yderligere modeller, som kan være nyttige for dem interesseret i denne pakke.

The package is enhanced by the following packages: r-cran-mcmc r-cran-mnp
r-cran-metrics
GNU R evaluation metrics for machine learning
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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-maskinlæring af sammenligningsproblemer
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Denne GNU R-pakke tilbyder en samling af kunstige og virkelige sammenligningsproblemer for maskinlæring, inklusive f.eks. flere datasæt fra UCI-arkivet.

r-cran-mlr
Machine learning in GNU R
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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
GNU R-pakke for tilpasning af multinomial probit-modeller (MNP)
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MNP er en R-pakke som tilpasser Bayesian Multinomial Probit-modeller (MNP) via Markov chain Monte Carlo (MCMC). Sammen med den gængse multinomial probit-model, så kan den også tilpasse modeller med forskellige valgsæt for hver observation og fuldstændig eller delvis ordning af alle tilgængelige alternativer. Estimeringen er baseret på den effektive marginal algoritme for dataforøgning, som er udviklet af Imai og van Dyk (2004).

r-cran-msm
GNU R Multi-state Markov og skjulte Markovmodeller i sammenhængende tid
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Funktioner for tilpasning af generel Markov i sammenhængende tid og skjulte Markov flertilstandsmodeller til data i længderetningen. Både overgangsrater for Markov og den skjulte Markov-resultatproces kan modelleres i form af kovariater. Et udvalg af observationsskemaer er understøttet, inklusive processer observeret på arbitrære tidspunkter, fuldstændig-observerede processer, og censorerede tilstande.

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
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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
Metapakke til at installere alle ROOT-pakker
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ROOT-systemet tilbyder et sæt af OO-rammeværktøjer med al den funktionalitet, som er krævet for at håndtere og analysere store mængder af data effektivt.

Med dataene defineret som et sæt af objekter, kan specialiserede lagermetoder give direkte adgang til de separate attributter for de valgte objekter, uden at skulle røre den store mængde af data. Inkluderet er historgrammetoder i 1, 2 og 3 dimensioner, kurvetilpasning, funktionsevaluering, minimering, grafik- og visualiseringsklasser, der giver mulighed for nemt at oprette et analyseprogram, som kan forespørge og behandle dataene interaktivt eller i jobtilstand.

Kommandosproget, skriptsproget (eller makro) og programmeringssproget er alle C++, takket være den indbyggede CINT C++-fortolker. Denne fortolker fjerner den tidsforbrugende kompilering/lænke-cyklus, hvilket giver mulighed for hurtigere udarbejdelse af prototyper for makroer og tilbyder et godt miljø til at lære C++. Hvis yderligere ydelse er krævet, kan de interaktive makroer kompileres med en C++-kompiler.

Systemet er blevet designet på en sådan måde, at du kan forespørge dens databaser parallelt på MPP-maskiner eller på klynger af arbejdsstationer eller kraftige pc'er. ROOT er et åbent system, som dynamisk kan udvides ved at lænke eksterne biblioteker. Dette gør ROOT til en ekstraordinær platform hvorpå der kan bygges dataindhentelse, simulering og dataanalyse.

Denne pakke er en metapakke til at sikre installationen af alle mulige ROOT-pakker på et system.

scilab-ann
Scilab module for artificial neural networks
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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 - grundkomponenter
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Torch er en videnskabelig beregningsramme med bred understøttelse for maskinlæringsalgoritmer som sætter GPU'er først. Rammen er nem at bruge og effektivt, takket være et nemt og hurtigt skriptsprog, LuaJIT og en underliggende C/CUDA-implementering.

Et overblik over grundfunktioner:

  • en funktionsrig N-dimensionel tabel
  • masser af rutiner for indeksering, opdeling, transposing, ...
  • imponerende grænseflade til C, via LuaJIT
  • lineær algebrarutiner
  • neuralt netværk og energibaserede modeller
  • numeriske optimeringsrutiner
  • Hurtig og effektiv GPU-understøttelse
  • Kan indlejres, med porte til iOS-, Android- og FPGA-motorer

Formålet med Torch er at have maksimal fleksibilitet og hastighed i bygning af dine videnskabelige algoritmer mens processen er ekstrem simpel. Torch har et stort økosystem af fællesskabsdrevne pakker i maskinlæring, computervision, signalbehandling, parallel behandling, billede, video, lyd og netværk blandt andre og bygget oven på Lua- fællesskabet.

I hjertet af Torch er det populære neurale netværk og optimeringsbiblioteker som er simple at bruge, mens de har maksimal fleksibilitet i implementering af komplekse neurale netværkstopologier. Du kan bygge arbitrære grafer over neurale netværk og parallelisere dem over CPu'er og GPU'er på en effektiv måde.

Denne pakke er en metapakke, som henter de følgende grundlæggende og frie moduler for dig: cwrap, paths, sys, xlua, torch7, nn, graph, nngraph, optim, sundown, dok, trepl, image.

Bemærk at cutorch (CUDA-motor for torch) og cunn (CUDA-motor for neuralt netværk) ikke er til stede i denne metapakke - de vil blive medtaget i metapakken torch-core-contrib engang i fremtiden.

toulbar2
Præcis kombinatorisk optimering for grafiske modeller
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Toulbar2 er et præcist diskret optimeringsværktøj for grafiske modeller såsom Cost Function Networks, Markov Random Fields, Weighted Constraint Satisfaction Problems og Bayesian Nets.

vowpal-wabbit
hurtig og skalérbar algoritme til netbaseret maskinlæring
Maintainer: Yaroslav Halchenko
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Vowpal Wabbit er en algoritme til hurtig, netbaseret maskinlæring. Kerne- algoritmen er en specialiseret gradientnedstigning (GD) på en tabsfunktion (flere er tilgængelige). VW-funktioner:

  • fleksibel specifikation af inddata
  • hurtig læring
  • skalérbarhed (fast hukommelsesforbrug, egner sig til distribuerede beregninger)
  • funktionsbindinger
Screenshots of package vowpal-wabbit
weka
Algoritmer for maskinlæring for dataindekseringsopgaver
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Weka er en samling af algoritmer for maskinlæring i Java, som enten kan bruges fra kommandolinjen eller kaldes fra din egen javakode. Weka er også velegnet for udvikling af ny maskinlæringsskemaer.

Implementerede skemaer dækker beslutningstræ-induktorer, regeltilegnelse, modeltræ-generatorer, understøtter vektormaskiner, lokalt vægtet regression, instansbaseret læring, »bagging«, »boosting« og stabling (»stacking«). Også inkluderet er klyngemetoder og en tilegnelse af associationsregler. Udover reelle læringsskemaer indeholder Weka også en stor række af værktøjer, som kan bruges for præbehandling af datasæt.

Denne pakke indeholder de binære filer og eksempler.

yap
Højtydende Prologsystem
Maintainer: Ralf Treinen
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Højtydende Prologkompilere udviklet på LIACC/Universidade do Porto og på COPPE Sistemas/UFRJ. YAP Prolog-motoren er baseret i Warren Abstract Machine, men har flere optimeringer for bedre ydelse. YAP følger Edinburghtraditionen og er stort set kompatibel med ISO-Prolog-standarden og med Quintus og SICStus Prolog.

YAP har en begrænsningsløser til reelle tal og understøttelse for begrænsningshåndteringsregler (CHR).

Official Debian packages with lower relevance

ask
Adaptive Sampling Kit for store eksperimentelle rum
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Adaptive Sampling Kit (ASK) er et værktøjssæt for sampling af store eksperimentelle rum. Når rummet er lille, kan svaret måles for hvert punkt i rummet. Når rummet er stort, er en omfattende måling enten ikke mulig i form af kørselstid eller simpelthen ikke praktisk. ASK forsøger at finde en god tilnærmelse for svaret ved kun at sample en lille del af rummet. ASK har flere aktive læringsalgoritmer til at prioritere undersøgelsen af de interessante dele af det eksperimentelle rum.

libdlib-dev
C++ toolkit for machine learning and computer vision - development
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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.

libdlpack-dev
Open In Memory Tensor Structure
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DLPack is an open in-memory tensor structure to for sharing tensor among frameworks. DLPack enables

  • Easier sharing of operators between deep learning frameworks.
  • Easier wrapping of vendor level operator implementations, allowing collaboration when introducing new devices/ops.
  • Quick swapping of backend implementations, like different version of BLAS
  • For final users, this could bring more operators, and possibility of mixing usage between frameworks.

DLPack do not intend to implement of Tensor and Ops, but instead use this as common bridge to reuse tensor and ops across frameworks.

libfannj-java
FannJ - en Javabinding til Fast Artificial Neural Network (FANN) C-biblioteket
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Brug FannJ hvis du har en eksisterende ANN fra FANN-projektet (libfann2), som du ønsker at tilgå fra Java. Der er flere værktøjer for den grafiske brugerflade, som vil hjælpe dig med at oprette og træne en ANN.

libfclib-dev
read and write problems from the Friction Contact Library (headers)
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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)
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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.

One can choose to build Intel MKL-DNN without binary dependency. The resulting version will be fully functional, however performance of certain convolution shapes and sizes and inner product relying on SGEMM function may be suboptimal.

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

libmrgingham-dev
Chessboard finder for visual calibration routines
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Given an observed image containing a chessboard or a grid of circles, mrgingham locates the board in the image, and precisely computes the location of the chessboard corners (or circle centers). This is similar to the routines in OpenCV, but is faster and more robust.

This package provides the development C++ libraries

libxgboost-predictor-java
Java implementation of XGBoost predictor for online prediction tasks
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XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way. The same code runs on major distributed environment (Kubernetes, Hadoop, SGE, MPI, Dask) and can solve problems beyond billions of examples.

libxsmm-dev
Bibliotek for matrix-operationer og dyb lærings-primitiver
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LIBXSMM er et bibliotek målrettet Intel Architecture for specialiserede tætte og tynde matrixoperationer og dyb lærings-primitiver.

Denne pakke indeholder værktøjerne, de statiske biblioteker og teksthovedfilerne.

python3-hdmedians
Høj-dimensionelle medianer i Python 3
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Diverse definitioner for en høj-dimensionel median findes og denne Pythonpakke tilbyder et antal hurtige implementeringer af disse definitioner. Medianer er ekstremt nyttige på grund af deres høje nedbrudspunkt (op til 50 % forurening) og har et antal pæne anvendelser indenfor maskinlæring, computersyn og høj-dimensionel statistik.

Denne pakke har i øjeblikket implementeringer af medoid og geometrisk median med understøttelse for manglende data via NaN.

python3-imblearn
Bibliotek der tilbyder teknikker til resampling
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Imbalanced-learn er en Pythonpakke, der tilbyder et antal teknikker til resampling ofte brugt i datasæt, der viser stærk mellem-klasse ubalance.

Er kompatibel med scikit-learn og er en del af scikit-learn-contrib-projekterne.

python3-liac-arff
library for reading and writing ARFF files in Python
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The liac-arff module implements functions to read and write ARFF files in Python. It was created in the Connectionist Artificial Intelligence Laboratory (LIAC), which takes place at the Federal University of Rio Grande do Sul (UFRGS), in Brazil.

ARFF (Attribute-Relation File Format) is an file format specially created for describing datasets which are used commonly for machine learning experiments and software. This file format was created to be used in WEKA, the best representative software for machine learning automated experiments.

python3-mrgingham
Chessboard finder for visual calibration routines
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Given an observed image containing a chessboard or a grid of circles, mrgingham locates the board in the image, and precisely computes the location of the chessboard corners (or circle centers). This is similar to the routines in OpenCV, but is faster and more robust.

This package provides the Python interfaces

science-numericalcomputation
Debian Science Numerical Computation-pakker
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Denne metapakke vil installere Debian Science-pakker nyttige til numerisk beregning. Pakkerne tilbyder en tabelorienteret beregning og et visualiseringssystem til videnskabelig beregning og dataanalyse. Disse pakker svarer til kommercielle systemer såsom Matlab og IDL.

science-statistics
Debians videnskabelige statistikpakker
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Denne metapakke er en del af Debians Pure Blend »Debian Science« og installerer pakker relateret til statistik. Denne opgave er en generel opgave, som kan være nyttig for videnskabelig arbejde. Den afhænger af en masse R-pakker samt andre værktøjer, som er nyttige til at udføre statistik. Derudover foreslås Videnskabelig matematik-opgaven som valgfri installation af alle matematikrelaterede programmer.

science-typesetting
Debian Science - opsætningspakker
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Denne metapakke vil installere Debian Science-pakker relateret til opsætning. Du er måske også interesseret i deb-mærket use::typesetting.

Debian packages in contrib or non-free

caffe-cuda
Fast, open framework for Deep Learning (Meta)
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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)
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Version: 2.2.3-1

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.

streamlit
fast way to build custom ML tools
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Version: 0.56.0-1

Streamlit lets you create apps for your machine learning projects with deceptively simple Python scripts. It supports hot-reloading, so your app updates live as you edit and save your file. No need to mess with HTTP requests, HTML, JavaScript, etc. All you need is your favorite editor and a browser.

Remark of Debian Science team: Needed for chime which is COVID-19 relevant.

Help for packaging is needed.

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 236780