Debian Science Machine Learning packages

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.

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.

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.

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 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.

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.

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.

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.

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.

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).

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

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.

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 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 !

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).

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.

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.

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.

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.

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.

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).

Denne pakke består af et lille udvidelsesbibliotek for optimerede rumlige matrix-operationer med autograd-understøttelse.

Denne pakke installerer biblioteket for Python 3.

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.

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".

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.

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

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.

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).

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

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.

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.

Denne GNU R-pakke tilbyder en samling af kunstige og virkelige sammenligningsproblemer for maskinlæring, inklusive f.eks. flere datasæt fra UCI-arkivet.

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.

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).

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.

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.

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

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.

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.

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.

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.

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.

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

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 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.

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.

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.

Modulet liac-arff implementerer funktioner til at læse og skrive ARFF-filer i Python. Det blev oprettet i Connectionist Artificial Intelligence Laboratory (LIAC), der foregår på Federal University of Rio Grande do Sul (UFRGS), i Brasilien.

ARFF (Attribute-Relation File Format) er et filformat specifikt oprettet til at beskrive datasæt, der ofte bruges til eksperimenter indenfor maskinlæring og programmer. Dette filformat blev oprettet til brug i WEKA, det bedste repræsentative program for automatiserede eksperimenter indenfor maskinlæring.

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

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.

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.

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

Adaptive Sampling Kit (ASK) is a toolkit for sampling big experimental spaces. When the space is small, the response can be measured for every point in the space. When the space is large, doing an exhaustive measurement is either not possible in terms of execution time or simply not practical. ASK tries to find good approximations of the response by sampling only a small fraction of the space. ASK features multiple active learning algorithms to prioritize the exploration of the interesting parts of the experimental space.

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.

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.

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.

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.

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.

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.

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.

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.

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

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 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.

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.

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.

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 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.