Debian Science Machine Learning packages

AutoClass löst das Problem der automatischen Entdeckung von Klassen in Daten (manchmal auch »Clustering« oder »unüberwachtes Lernen« genannt), das sich von der Erzeugung von Klassenbeschreibungen aus gekennzeichneten Beispielen (»überwachtes Lernen« genannt) unterscheidet. Es soll die »natürlichen« Klassen in Daten entdecken. AutoClass kann auf Beobachtungen von Dingen angewandt werden, die durch eine Attributmenge ausgedrückt werden können, ohne sich auf andere Dinge zu beziehen. Die Datenwerte der einzelnen Attribute müssen entweder Nummern oder Elemente einer festen Menge von Symbolen sein. Bei numerischen Daten muss ein Messfehler angegeben werden.

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 CPU_ONLY version of caffe:

• caffe-tools-cpu
• libcaffe-cpu*

• python3-caffe-cpu And suggests these packages:

• libcaffe-cpu-dev

• caffe-doc

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

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.

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.

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 ist eine Bibliothek für Klassifikatoren, die durch Linearkombination lernen, und ist gedacht für große Anwendungen. Sie unterstützt Support Vector Machines (SVM) mit den Verlustfunktionen L2 und L1, logistische Regression, Multi-Class-Klassifizierung und auch Linear Programming Machines (L1-normalisierte SVMs). Die Komplexität steigt linear mit der Anzahl an Trainingsbeispielen und ist damit eine der schnellsten SVM-Löser.

Dieses Paket enthält die Header-Dateien und die statischen Bibliotheken.

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.

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.

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, eine Bibliothek für maschinelles Lernen, ist ein benutzerfreundliches Paket zur Klassifizierung mit Stützvektoren, Regression und Verteilungsschätzungen (one-class SVM). Sie unterstützt die Klassifizierung in mehrere Klassen, Wahrscheinlichkeitsausgaben und Parameterauswahl.

Dieses Paket enthält die Header-Dateien für die Entwicklung.

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.)

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.

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.

"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.

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.

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.

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.

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 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.

Das Paket MCL ist eine Implementierung des MCL-Algorithmus. Es stellt Werkzeuge zur Manipulation dünn besetzter Matrizen (der essentiellen Datenstruktur im MCL-Algorithmus) und zur Durchführung von Cluster-Experimenten zur Verfügung.

MCL wird derzeit in Wissenschaften wie Biologie (Erkennung von Proteinfamilien, Genomforschung), Informatik (Clustering von Knoten in Peer-to-Peer-Netzwerken) und Sprachwissenschaften (Textanalyse) verwendet.

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

This package provides function to work with genetic algorithms in Octave, a numerical computation software. It provides the ga() function, which works similarly to other optimization functions in Octave.

This Octave add-on package is part of the Octave-Forge project.

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.

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.

This package contains the Python 3 bindings 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).

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.

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.

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.

This package contains Python 3 bindings for the OpenCV (Open Computer Vision) library.

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.

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.

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.

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.

PyTorch is a Python package that provides two high-level features:

(1) Tensor computation (like NumPy) with strong GPU acceleration (2) Deep neural networks built on a tape-based autograd system

You can reuse your favorite Python packages such as NumPy, SciPy and Cython to extend PyTorch when needed.

This is the CPU-only version of PyTorch (Python interface).

This package consists of a small extension library of optimized sparse matrix operations with autograd support.

This package installs the library for Python 3.

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 exports the functionality of the VIGRA library to Python3.

Dieses Paket entstand, um den robusten TAO-Algorithmus für Neuronale Netze für die Anwender von R bereitzustellen. Er wurde weiter entwickelt und kann von Interesse sein sowohl für Anwender, die ihre eigenen Trainings-Algorithmen implementieren wollen, als auch für diejenigen, deren Bedürfnisse sich auf die Anwendung beschränken.

Das Paket bayesm behandelt viele wichtige Modelle von Anwendungen in Marketing und Mikroökonometrie. Das Paket enthält:

• Bayes-Regression (uni- oder multivariate dep var)
• Multinomiales Logit- (MNL) und Multinomiales Probit-Modell (MNP)
• Multivariates Probit-Modell,
• Multivariate normale Mischverteilung
• Mehrebenenanalyse mit normaler A-priori-Wahrscheinlichkeit und Kovariaten
• »Hierarchical Multinomial Logits« mit Mischverteilungen von normalen Anfangswahrscheinlichkeit und Kovariaten
• Bayessche Analyse von Daten einer entscheidungsbasierten Conjoint-Analyse
• Bayessche Aufbereitung von linearen Instrumentvariablen-Modelle
• Analyse von multivariaten Ordinal-Befragungsdaten mit in Skalen verwendete Heterogenität (siehe Rossi et al, JASA (01)).

Weiterführende Literatur: »Bayesian Statistics and Marketing« von Allenby, McCulloch und Rossi.

Das class-Paket bietet Funktionen und Datensätze zur Unterstützung von Kapitel 12 zum Thema »Klassifizierung« im Buch »Modern Applied Statistics with S« (4. Ausgabe) von W.N. Venables und B.D. Ripley. Die folgende URL enthält weitere Einzelheiten über das Buch:

 http://www.stats.ox.ac.uk/pub/MASS4

Dieses Paket enthält Funktionen und Datensätze zur Cluster-Analyse, die ursprünglich von Peter Rousseeuw, Anja Struyf und Mia Hubert geschrieben wurden.

Dieses Paket ist Teil des Satzes der Pakete, die von »R Core« empfohlen werden und wird mit Original-Quellcode-Releases von R ausgeliefert.

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).

Das MASS-Paket bietet Funktionen und Datensätze zur Unterstützung des Buchs »Modern Applied Statistics with S« (4. Ausgabe) von W.N. Venables und B.D. Ripley. Die folgende URL weist zu weiteren Einzelheiten über das Buch:

 URL: http://www.stats.ox.ac.uk/pub/MASS4

This is a set of routines for GNU R that implement various statistical and econometric models using Markov chain Monte Carlo (MCMC) estimation, which allows "solving" models that would otherwise be intractable with traditional techniques, particularly problems in Bayesian statistics (where one or more "priors" are used as part of the estimation procedure, instead of an assumption of ignorance about the "true" point estimates), although MCMC can also be used to solve frequentist statistical problems with uninformative priors. MCMC techniques are also preferable over direct estimation in the presence of missing data.

Currently implemented are a number of ecological inference (EI) routines (for estimating individual-level attributes or behavior from aggregate data, such as electoral returns or census results), as well as models for traditional linear panel and cross-sectional data, some visualization routines for EI diagnostics, two item-response theory (or ideal-point estimation) models, metric, ordinal, and mixed-response factor analysis, and models for Gaussian (linear) and Poisson regression, logistic regression (or logit), and binary and ordinal-response probit models.

The suggested packages (r-cran-bayesm, -eco, and -mnp) contain additional models that may also be useful for those interested in this package.

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.

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.

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 is an R package that fits Bayesian Multinomial Probit (MNP) models via Markov chain Monte Carlo (MCMC). Along with the standard multinomial probit model, it can also fit models with different choice sets for each observation and complete or partial ordering of all the available alternatives. The estimation is based on the efficient marginal data augmentation algorithm that is developed by Imai and van Dyk (2004).

Funktionen zur Parameterbestimmung von generellen zeitkontinuierlichen und Hidden-Markov-Multi-State-Modellen zu längslaufenden Daten. Sowohl Übergangsraten in Markov-Modellen als auch der Hidden-Markov-Ausgabeprozess können modelliert werden, innerhalb der Gesetze der Zusatzwerte (covariates). Eine Auswahl von Beobachtungsschemata wird unterstützt, inklusive Beobachtung von Prozessen zu beliebigen Zeiten, vollständig überwachte Prozesse und zensierte Stadien.

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.

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

Weka is a collection of machine learning algorithms in Java that can either be used from the command-line, or called from your own Java code. Weka is also ideally suited for developing new machine learning schemes.

Implemented schemes cover decision tree inducers, rule learners, model tree generators, support vector machines, locally weighted regression, instance-based learning, bagging, boosting, and stacking. Also included are clustering methods, and an association rule learner. Apart from actual learning schemes, Weka also contains a large variety of tools that can be used for pre-processing datasets.

This package contains the binaries and examples.

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.

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.

Use FannJ if you have an existing ANN from the FANN project (libfann2) that you would like to access from Java. There are several GUI tools that will help you create and train an 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.

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.

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

This package contains the tools, static libraries and header files.

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.

imbalanced-learn is a Python package offering a number of re-sampling techniques commonly used in datasets showing strong between-class imbalance.

It is compatible with scikit-learn and is part of scikit-learn-contrib projects.

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.

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

Dieses Metapaket installiert nützliche Pakete für numerische Berechnungen. Die Pakete bieten ein Array-orientiertes Berechnungs- und Visualisierungssystem für wissenschaftliches Rechnen und Datenanalyse. Sie ähneln kommerziellen Systemen wie Matlab und IDL.

Dieses Metapaket ist Teil des Debian Pure Blend »Debian Science« und installiert Pakete im Zusammenhang zum Begriff Statistiken. Dieser Task ist ein allgemeiner Task, welcher nützlich ist für alle wissenschaftlichen Arbeiten. Das Paket hängt von einer Reihe von R-Paketen ab, genau so wie von anderen Werkzeugen, die nützlich für die Anwendung von Statistik sind. Des weiteren ist der Task Mathematik-Wissenschaften empfohlen, um optionale Mathematik-Software zu installieren.

Dieses Metapaket installiert Debian-Science-Pakete für den Schriftsatz. Vielleicht sind auch Pakete mit der Kennzeichnung use::typesetting interessant für Sie.

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.

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.

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.