Summary
Machine Learning
Debian Science Machine Learning packages
This metapackage will install packages useful for machine learning.
Included packages range from knowledge-based (expert) inference
systems to software implementing the advanced statistical methods
that currently dominate the field.
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
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Debian Science Machine Learning packages
Official Debian packages with high relevance
autoclass
automatic classification or clustering
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Versions of package autoclass |
Release | Version | Architectures |
bullseye | 3.3.6.dfsg.1-2 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
sid | 3.3.6.dfsg.2-2 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
trixie | 3.3.6.dfsg.2-2 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
bookworm | 3.3.6.dfsg.2-1 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
buster | 3.3.6.dfsg.1-1 | amd64,arm64,armhf,i386 |
jessie | 3.3.6.dfsg.1-1 | amd64,armel,armhf,i386 |
stretch | 3.3.6.dfsg.1-1 | amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x |
Debtags of package autoclass: |
field | mathematics |
interface | commandline |
role | program |
scope | utility |
use | organizing |
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License: DFSG free
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AutoClass solves the problem of automatic discovery of classes in data
(sometimes called clustering, or unsupervised learning), as distinct
from the generation of class descriptions from labeled examples
(called supervised learning). It aims to discover the "natural"
classes in the data. AutoClass is applicable to observations of
things that can be described by a set of attributes, without referring
to other things. The data values corresponding to each attribute are
limited to be either numbers or the elements of a fixed set of
symbols. With numeric data, a measurement error must be provided.
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caffe-cpu
Fast, open framework for Deep Learning (Meta)
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Versions of package caffe-cpu |
Release | Version | Architectures |
buster | 1.0.0+git20180821.99bd997-2 | amd64,arm64,armhf,i386 |
stretch | 1.0.0~rc4-1 | amd64,arm64,armel,i386,mips,mips64el,mipsel,ppc64el,s390x |
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License: DFSG free
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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 CPU_ONLY version of caffe:
Note, this CPU_ONLY version cannot co-exist with the CUDA version.
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gprolog
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Versions of package gprolog |
Release | Version | Architectures |
trixie | 1.4.5.0-3 | amd64,i386 |
jessie | 1.3.0-6.1 | amd64,i386 |
bullseye | 1.4.5.0-3 | amd64,i386 |
bookworm | 1.4.5.0-3 | amd64,i386 |
sid | 1.4.5.0-3 | amd64,i386 |
Debtags of package gprolog: |
devel | compiler, interpreter, lang:prolog |
interface | commandline |
role | program |
scope | utility |
suite | gnu |
works-with | software:source |
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License: DFSG free
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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.
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libcv-dev
??? missing short description for package libcv-dev :-(
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Versions of package libcv-dev |
Release | Version | Architectures |
jessie | 2.4.9.1+dfsg-1+deb8u1 | amd64,armel,armhf,i386 |
jessie-security | 2.4.9.1+dfsg-1+deb8u2 | amd64,armel,armhf,i386 |
stretch-security | 2.4.9.1+dfsg1-2+deb9u1 | amd64,arm64,armel,armhf,i386 |
stretch | 2.4.9.1+dfsg1-2 | amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x |
upstream | 4.10.0 |
Debtags of package libcv-dev: |
devel | library |
role | devel-lib |
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License: DFSG free
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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
??? missing short description for package libevocosm-dev :-(
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Versions of package libevocosm-dev |
Release | Version | Architectures |
jessie | 4.0.2-3 | amd64,armel,armhf,i386 |
stretch | 4.0.2-3.1 | amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x |
Debtags of package libevocosm-dev: |
devel | library |
role | devel-lib |
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License: DFSG free
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libfann-dev
Development libraries and header files for FANN
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Versions of package libfann-dev |
Release | Version | Architectures |
buster | 2.2.0+ds-5 | amd64,arm64,armhf,i386 |
bullseye | 2.2.0+ds-6 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
sid | 2.2.0+ds-8 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
trixie | 2.2.0+ds-8 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
bookworm | 2.2.0+ds-8 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
stretch | 2.2.0+ds-3 | amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x |
jessie | 2.1.0~beta+dfsg-1 | amd64,armel,armhf,i386 |
Debtags of package libfann-dev: |
devel | lang:c, library |
role | devel-lib |
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License: DFSG free
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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.
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libga-dev
C++ Library of Genetic Algorithm Components
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Versions of package libga-dev |
Release | Version | Architectures |
trixie | 2.4.7-6 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
bullseye | 2.4.7-4 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
buster | 2.4.7-4 | amd64,arm64,armhf,i386 |
stretch | 2.4.7-4 | amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x |
jessie | 2.4.7-3.1 | amd64,armel,armhf,i386 |
sid | 2.4.7-6 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
bookworm | 2.4.7-6 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
Debtags of package libga-dev: |
devel | library |
role | devel-lib |
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License: DFSG free
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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.
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liblinear-dev
Development libraries and header files for LIBLINEAR
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Versions of package liblinear-dev |
Release | Version | Architectures |
experimental | 2.43+dfsg-1 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
buster | 2.1.0+dfsg-4 | amd64,arm64,armhf,i386 |
bullseye | 2.3.0+dfsg-5 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
bookworm | 2.3.0+dfsg-5 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
trixie | 2.3.0+dfsg-5 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
stretch | 2.1.0+dfsg-2 | amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x |
sid | 2.3.0+dfsg-5 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
jessie | 1.8+dfsg-4 | amd64,armel,armhf,i386 |
upstream | 2.4.7 |
Debtags of package liblinear-dev: |
devel | lang:c, lang:c++, library |
role | devel-lib |
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License: DFSG free
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LIBLINEAR is a library for learning linear classifiers for large scale
applications. It supports Support Vector Machines (SVM) with L2 and L1
loss, logistic regression, multi class classification and also Linear
Programming Machines (L1-regularized SVMs). Its computational complexity
scales linearly with the number of training examples making it one of
the fastest SVM solvers around.
This package contains the header files and static libraries.
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libmlpack-dev
intuitive, fast, scalable C++ machine learning library (development libs)
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Versions of package libmlpack-dev |
Release | Version | Architectures |
sid | 4.5.1-1 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
jessie | 1.0.10-1 | amd64,armel,armhf,i386 |
stretch | 2.1.1-1 | amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x |
buster | 3.0.4-1 | amd64,arm64,armhf,i386 |
bullseye | 3.4.2-1 | amd64,arm64,i386,ppc64el,s390x |
trixie | 4.5.1-1 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
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License: DFSG free
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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.
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libocas-dev
Development libraries and header files for LIBOCAS
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Versions of package libocas-dev |
Release | Version | Architectures |
sid | 0.97+dfsg-8 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
trixie | 0.97+dfsg-8 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
bullseye | 0.97+dfsg-6 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
stretch | 0.97+dfsg-3 | amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x |
jessie | 0.97-1 | amd64,armel,armhf,i386 |
bookworm | 0.97+dfsg-8 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
buster | 0.97+dfsg-5 | amd64,arm64,armhf,i386 |
Debtags of package libocas-dev: |
devel | lang:c, library |
role | devel-lib |
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License: DFSG free
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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.
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libroot-math-mlp-dev
??? missing short description for package libroot-math-mlp-dev :-(
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Versions of package libroot-math-mlp-dev |
Release | Version | Architectures |
jessie | 5.34.19+dfsg-1.2 | amd64,armel,armhf,i386 |
Debtags of package libroot-math-mlp-dev: |
devel | library |
role | devel-lib |
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License: DFSG free
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libroot-montecarlo-vmc-dev
??? missing short description for package libroot-montecarlo-vmc-dev :-(
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Versions of package libroot-montecarlo-vmc-dev |
Release | Version | Architectures |
jessie | 5.34.19+dfsg-1.2 | amd64,armel,armhf,i386 |
Debtags of package libroot-montecarlo-vmc-dev: |
devel | library |
role | devel-lib |
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License: DFSG free
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libroot-tmva-dev
??? missing short description for package libroot-tmva-dev :-(
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Versions of package libroot-tmva-dev |
Release | Version | Architectures |
jessie | 5.34.19+dfsg-1.2 | amd64,armel,armhf,i386 |
Debtags of package libroot-tmva-dev: |
devel | library |
role | devel-lib |
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License: DFSG free
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libshark-dev
??? missing short description for package libshark-dev :-(
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Versions of package libshark-dev |
Release | Version | Architectures |
stretch | 3.1.3+ds1-2 | amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x |
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License: DFSG free
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libshogun-dev
Large Scale Machine Learning Toolbox
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Versions of package libshogun-dev |
Release | Version | Architectures |
jessie | 3.2.0-7.3 | amd64,armel,armhf,i386 |
buster | 3.2.0-8 | amd64,arm64,armhf,i386 |
Debtags of package libshogun-dev: |
devel | library |
role | devel-lib |
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License: DFSG free
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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.
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libsvm-dev
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Versions of package libsvm-dev |
Release | Version | Architectures |
sid | 3.24+ds-6 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
bookworm | 3.24+ds-6 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
bullseye | 3.24+ds-6 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
stretch | 3.21+ds-1.1 | amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x |
experimental | 3.25+ds-1~exp1 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
jessie | 3.12-1 | amd64,armel,armhf,i386 |
trixie | 3.24+ds-6 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
buster | 3.21+ds-1.2 | amd64,arm64,armhf,i386 |
upstream | 3.35 |
Debtags of package libsvm-dev: |
devel | library |
role | devel-lib |
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License: DFSG free
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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.
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libtorch3-dev
State of the art machine learning library - development files
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Versions of package libtorch3-dev |
Release | Version | Architectures |
buster | 3.1-2.2 | amd64,arm64,armhf,i386 |
stretch | 3.1-2.2 | amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x |
jessie | 3.1-2.1 | amd64,armel,armhf,i386 |
Debtags of package libtorch3-dev: |
devel | library |
role | devel-lib |
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License: DFSG free
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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.)
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libvigraimpex-dev
development files for the C++ computer vision library
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Versions of package libvigraimpex-dev |
Release | Version | Architectures |
bullseye | 1.11.1+dfsg-8 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
sid | 1.12.1+dfsg-1 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
trixie | 1.12.1+dfsg-1 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
bookworm | 1.11.1+dfsg-11 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
buster | 1.10.0+git20160211.167be93+dfsg1-2 | amd64,arm64,armhf,i386 |
stretch | 1.10.0+git20160211.167be93+dfsg-2 | amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x |
jessie | 1.9.0+dfsg-10 | amd64,armel,armhf,i386 |
Debtags of package libvigraimpex-dev: |
devel | lang:c++, library |
role | devel-lib |
works-with | image, image:raster |
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License: DFSG free
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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.
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lua-torch-graph
Graph Computation Package for Torch Framework
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Versions of package lua-torch-graph |
Release | Version | Architectures |
buster | 0~20161121-g37dac07-3 | all |
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License: DFSG free
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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.
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lua-torch-image
Image Load/Save Library for Torch Framework
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Versions of package lua-torch-image |
Release | Version | Architectures |
buster | 0~20170420-g5aa1881-7 | amd64,armhf |
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License: DFSG free
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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 .
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lua-torch-nn
Neural Network Package for Torch Framework
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Versions of package lua-torch-nn |
Release | Version | Architectures |
buster | 0~20171002-g8726825+dfsg-4 | all |
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License: DFSG free
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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
table s.
-
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.
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lua-torch-nngraph
Neural Network Graph Package for Torch Framework
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Versions of package lua-torch-nngraph |
Release | Version | Architectures |
buster | 0~20170208-g3ed3b9b-3 | all |
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License: DFSG free
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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.
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lua-torch-optim
Numeric Optimization Package for Torch Framework
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Versions of package lua-torch-optim |
Release | Version | Architectures |
buster | 0~20171127-ga5ceed7-1 | all |
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License: DFSG free
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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.
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lua-torch-trepl
REPL Package for Torch Framework
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Versions of package lua-torch-trepl |
Release | Version | Architectures |
buster | 0~20170619-ge5e17e3-7 | amd64,armhf,i386 |
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License: DFSG free
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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'.
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lua-torch-xlua
Lua Extension Package for Torch Framework
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Versions of package lua-torch-xlua |
Release | Version | Architectures |
buster | 0~20160719-g41308fe-7 | all |
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License: DFSG free
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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.
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mcl
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Versions of package mcl |
Release | Version | Architectures |
bookworm | 22-282+ds-2 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
trixie | 22-282+ds-2 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
jessie | 14-137-1 | amd64,armel,armhf,i386 |
stretch | 14-137-1 | amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x |
buster | 14-137+ds-3 | amd64,arm64,armhf,i386 |
bullseye | 14-137+ds-9 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
sid | 22-282+ds-2 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
Debtags of package mcl: |
field | mathematics |
role | program |
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License: DFSG free
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The MCL package is an implementation of the MCL algorithm, and offers
utilities for manipulating sparse matrices (the essential data
structure in the MCL algorithm) and conducting cluster experiments.
MCL is currently being used in sciences like biology (protein family
detection, genomics), computer science (node clustering in
Peer-to-Peer networks), and linguistics (text analysis).
The package is enhanced by the following packages:
zoem
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mrgingham
Chessboard finder for visual calibration routines
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Versions of package mrgingham |
Release | Version | Architectures |
sid | 1.24-2 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
bookworm | 1.22-1 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
trixie | 1.24-2 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
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License: DFSG free
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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
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octave-ga
genetic optimization code for Octave
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Versions of package octave-ga |
Release | Version | Architectures |
stretch | 0.10.0-2 | all |
sid | 0.10.4-1 | all |
jessie | 0.10.0-2 | all |
trixie | 0.10.4-1 | all |
bullseye | 0.10.2-1 | all |
bookworm | 0.10.3-2 | all |
buster | 0.10.0-6 | all |
Debtags of package octave-ga: |
devel | lang:octave, library |
role | devel-lib |
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License: DFSG free
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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.
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pgapack
??? missing short description for package pgapack :-(
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Versions of package pgapack |
Release | Version | Architectures |
jessie | 1.1.1-3 | amd64,armel,armhf,i386 |
Debtags of package pgapack: |
field | mathematics |
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License: DFSG free
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python3-amp
Atomistic Machine-learning Package (python 3)
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Versions of package python3-amp |
Release | Version | Architectures |
buster | 0.6.1-1 | amd64,arm64,armhf,i386 |
bullseye | 0.6.1-1 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
sid | 0.6.1-1 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
bookworm | 0.6.1-1 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
upstream | 4878fc892f2cbc5cd9f29f7a367d7b05bdeb6ee9 |
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License: DFSG free
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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.
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python3-fann2
Python 3 bindings for FANN
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Versions of package python3-fann2 |
Release | Version | Architectures |
trixie | 1.2.0+ds-4 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
stretch | 1.0.7-6 | amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x |
sid | 1.2.0+ds-4 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
bookworm | 1.2.0+ds-4 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
bullseye | 1.2.0+ds-2 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
buster | 1.1.2+ds-1 | amd64,arm64,armhf,i386 |
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License: DFSG free
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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.
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python3-genetic
genetic algorithms in Python
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Versions of package python3-genetic |
Release | Version | Architectures |
bullseye | 0.1.1b+git20170527.98255cb-2 | all |
bookworm | 0.1.1b+git20170527.98255cb-3 | all |
trixie | 0.1.1b+git20170527.98255cb-4 | all |
sid | 0.1.1b+git20170527.98255cb-4 | all |
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License: DFSG free
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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 !
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python3-keras
deep learning framework running on Theano or TensorFlow
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Versions of package python3-keras |
Release | Version | Architectures |
buster | 2.2.4-1 | all |
bullseye | 2.3.1+dfsg-3 | all |
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License: DFSG free
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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).
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python3-lasagne
deep learning library build on the top of Theano (Python3 modules)
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Versions of package python3-lasagne |
Release | Version | Architectures |
buster | 0.1+git20181019.a61b76f-1 | all |
stretch | 0.1+git20160728.8b66737-2 | all |
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License: DFSG free
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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.
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python3-mdp
Modular toolkit for Data Processing
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Versions of package python3-mdp |
Release | Version | Architectures |
bullseye | 3.6-1.1 | all |
sid | 3.6-9 | all |
trixie | 3.6-9 | all |
bookworm | 3.6-2 | amd64,arm64,mips64el,ppc64el |
jessie | 3.3-2 | all |
stretch | 3.5-1 | all |
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License: DFSG free
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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.
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python3-mlpy
high-performance Python package for predictive modeling
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Versions of package python3-mlpy |
Release | Version | Architectures |
bullseye | 3.5.0+ds-1.2 | all |
bookworm | 3.5.0+ds-2 | all |
sid | 3.5.0+ds-3 | all |
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License: DFSG free
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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.
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python3-opencv
Python 3 bindings for the computer vision library
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Versions of package python3-opencv |
Release | Version | Architectures |
trixie | 4.6.0+dfsg-14 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
bullseye | 4.5.1+dfsg-5 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
bookworm | 4.6.0+dfsg-12 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
sid | 4.6.0+dfsg-14 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
buster | 3.2.0+dfsg-6 | amd64,arm64,armhf,i386 |
upstream | 4.10.0 |
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License: DFSG free
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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.
Please cite:
Gary Bradski and Adrian Kaehler:
Learning OpenCV: Computer Vision with the OpenCV Library
(2008)
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python3-sklearn
Python modules for machine learning and data mining - Python 3
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Versions of package python3-sklearn |
Release | Version | Architectures |
trixie | 1.4.2+dfsg-7 | all |
bullseye | 0.23.2-5 | all |
buster | 0.20.2+dfsg-6 | all |
stretch | 0.18-5 | all |
bookworm | 1.2.1+dfsg-1 | all |
sid | 1.4.2+dfsg-7 | all |
upstream | 1.6.0 |
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License: DFSG free
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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.
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python3-statsmodels
Python3 module for the estimation of statistical models
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Versions of package python3-statsmodels |
Release | Version | Architectures |
sid | 0.14.4+dfsg-1 | all |
buster | 0.8.0-9 | all |
bookworm | 0.13.5+dfsg-7 | all |
bullseye | 0.12.2-1 | all |
stretch-backports | 0.8.0-9~bpo9+1 | all |
trixie | 0.14.4+dfsg-1 | all |
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License: DFSG free
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statsmodels Python3 module provides classes and functions for the
estimation of several categories of statistical models. These
currently include linear regression models, OLS, GLS, WLS and GLS
with AR(p) errors, generalized linear models for several distribution
families and M-estimators for robust linear models. An extensive list
of result statistics are available for each estimation problem.
Please cite:
Skipper Seabold and Josef Perktold:
Statsmodels: Econometric and statistical modeling with python
(eprint)
(2010)
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python3-thinc
Practical Machine Learning for NLP in Python
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Versions of package python3-thinc |
Release | Version | Architectures |
buster | 6.12.1-1 | amd64,arm64,armhf,i386 |
sid | 9.0.0-2 | amd64,arm64,armhf,i386,mips64el,riscv64,s390x |
bookworm | 8.1.7-1 | amd64,arm64,armhf,i386,mips64el,s390x |
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License: DFSG free
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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.
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python3-torch
Tensors and Dynamic neural networks in Python (Python Interface)
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Versions of package python3-torch |
Release | Version | Architectures |
bullseye | 1.7.1-7 | amd64,arm64,armhf,ppc64el,s390x |
sid | 2.5.1+dfsg-1 | amd64,arm64,ppc64el,riscv64,s390x |
bookworm | 1.13.1+dfsg-4 | amd64,arm64,ppc64el,s390x |
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License: DFSG free
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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).
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 Library of Optimized Autograd Sparse Matrix Operations
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Versions of package python3-torch-sparse |
Release | Version | Architectures |
sid | 0.6.18-2 | amd64,arm64,ppc64el,riscv64,s390x |
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License: DFSG free
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This package consists of a small extension library of optimized sparse matrix
operations with autograd support.
This package installs the library for Python 3.
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python3-vigra
Python3 bindings for the C++ computer vision library
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Versions of package python3-vigra |
Release | Version | Architectures |
bullseye | 1.11.1+dfsg-8 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
trixie | 1.12.1+dfsg-1 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
sid | 1.12.1+dfsg-1 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
bookworm | 1.11.1+dfsg-11 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
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License: DFSG free
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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.
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r-cran-amore
GNU R: A MORE flexible neural network package
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Versions of package r-cran-amore |
Release | Version | Architectures |
trixie | 0.2-16-2 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
sid | 0.2-16-2 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
bookworm | 0.2-16-2 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
jessie | 0.2-15-1 | amd64,armel,armhf,i386 |
stretch | 0.2-15-1 | amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x |
bullseye | 0.2-16-1 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
buster | 0.2-15-3 | amd64,arm64,armhf,i386 |
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License: DFSG free
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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".
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r-cran-bayesm
GNU R package for Bayesian inference
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Versions of package r-cran-bayesm |
Release | Version | Architectures |
stretch | 3.0-2-2 | amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x |
jessie | 2.2-5-1 | amd64,armel,armhf,i386 |
bookworm | 3.1-5+dfsg-1 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
buster | 3.1-1-1 | amd64,arm64,armhf,i386 |
bullseye | 3.1-4+dfsg-1 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
trixie | 3.1-6+dfsg-1 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
sid | 3.1-6+dfsg-1 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
Debtags of package r-cran-bayesm: |
field | mathematics, statistics |
suite | gnu |
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License: DFSG free
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The bayesm package covers many important models used in marketing and
micro-econometrics applications. The package includes:
- Bayes Regression (univariate or multivariate dep var)
- Multinomial Logit (MNL) and Multinomial Probit (MNP)
- Multivariate Probit,
- Multivariate Mixtures of Normals
- Hierarchical Linear Models with normal prior and covariates
- Hierarchical Multinomial Logits with mixture of normals prior and
covariates
- Bayesian analysis of choice-based conjoint data
- Bayesian treatment of linear instrumental variables models
- Analyis of Multivariate Ordinal survey data with scale usage heterogeneity
(as in Rossi et al, JASA (01)).
For further reference, consult the authors' book, Bayesian Statistics and
Marketing by Allenby, McCulloch and Rossi.
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r-cran-class
GNU R package for classification
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Versions of package r-cran-class |
Release | Version | Architectures |
bookworm | 7.3-21-1 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
stretch | 7.3-14-1 | amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x |
trixie | 7.3-22-2 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
sid | 7.3-22-2 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
buster | 7.3-15-1 | amd64,arm64,armhf,i386 |
jessie | 7.3-11-1 | amd64,armel,armhf,i386 |
bullseye | 7.3-18-1 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
Debtags of package r-cran-class: |
devel | lang:r |
role | shared-lib |
science | calculation, modelling |
use | analysing |
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License: DFSG free
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The class package provides functions and datasets to support chapter
12 on 'Classification' in the book 'Modern Applied Statistics with S'
(4th edition) by W.N. Venables and B.D. Ripley. The following URL
provides more details about the book:
URL: http://www.stats.ox.ac.uk/pub/MASS4
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r-cran-cluster
GNU R package for cluster analysis by Rousseeuw et al
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Versions of package r-cran-cluster |
Release | Version | Architectures |
stretch | 2.0.5-1 | amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x |
jessie | 1.15.3-1 | amd64,armel,armhf,i386 |
bookworm | 2.1.4-1 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
trixie | 2.1.8-1 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
sid | 2.1.8-1 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
buster | 2.0.7-1-1 | amd64,arm64,armhf,i386 |
bullseye | 2.1.1-1 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
Debtags of package r-cran-cluster: |
devel | lang:r, library |
field | statistics |
role | app-data |
suite | gnu |
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License: DFSG free
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This package provides functions and datasets for cluster analysis originally
written by Peter Rousseeuw, Anja Struyf and Mia Hubert.
This package is part of the set of packages that are 'recommended'
by R Core and shipped with upstream source releases of R itself.
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r-cran-gbm
GNU R package providing Generalized Boosted Regression Models
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Versions of package r-cran-gbm |
Release | Version | Architectures |
buster | 2.1.5-1 | amd64,arm64,armhf,i386 |
sid | 2.2.2-1 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
bookworm | 2.1.8.1-1 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
stretch | 2.1.1-1 | amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x |
jessie | 2.1-1 | amd64,armel,armhf,i386 |
bullseye | 2.1.8-1 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
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License: DFSG free
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This package implements extensions to Freund and Schapire's AdaBoost algorithm
and Friedman's gradient boosting machine. Includes regression methods for least
squares, absolute loss, t-distribution loss, quantile regression, logistic,
multinomial logistic, Poisson, Cox proportional hazards partial likelihood,
AdaBoost exponential loss, Huberized hinge loss, and Learning to Rank measures
(LambdaMart).
|
|
r-cran-mass
GNU R package of Venables and Ripley's MASS
|
Versions of package r-cran-mass |
Release | Version | Architectures |
trixie | 7.3-61-1 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
jessie | 7.3-34-1 | amd64,armel,armhf,i386 |
stretch | 7.3-45-1 | amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x |
buster | 7.3-51.1-1 | amd64,arm64,armhf,i386 |
bullseye | 7.3-53.1-1 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
bookworm | 7.3-58.2-1 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
sid | 7.3-61-1 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
Debtags of package r-cran-mass: |
devel | lang:r |
field | statistics |
suite | gnu |
|
License: DFSG free
|
The MASS package provides functions and datasets to support the book
'Modern Applied Statistics with S' (4th edition) by W.N. Venables and
B.D. Ripley. The following URL provides more details about the book:
URL: http://www.stats.ox.ac.uk/pub/MASS4
The package is enhanced by the following packages:
r-cran-pscl
|
|
r-cran-mcmcpack
R routines for Markov chain Monte Carlo model estimation
|
Versions of package r-cran-mcmcpack |
Release | Version | Architectures |
bullseye | 1.5-0-1 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
sid | 1.7-0-1 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
trixie | 1.7-0-1 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
stretch | 1.3-8-1 | amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x |
jessie | 1.3-3-1 | amd64,armel,armhf,i386 |
bookworm | 1.6-3-1 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
buster | 1.4-4-1 | amd64,arm64,armhf,i386 |
upstream | 1.7-1 |
Debtags of package r-cran-mcmcpack: |
devel | lang:r, library |
field | statistics |
role | app-data |
suite | gnu |
|
License: DFSG free
|
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.
|
|
r-cran-metrics
GNU R evaluation metrics for machine learning
|
Versions of package r-cran-metrics |
Release | Version | Architectures |
bullseye | 0.1.4-2 | all |
bookworm | 0.1.4-2 | all |
buster | 0.1.4-1 | all |
trixie | 0.1.4-2 | all |
sid | 0.1.4-2 | all |
|
License: DFSG free
|
An implementation of evaluation metrics in R that are commonly
used in supervised machine learning. It implements metrics for
regression, time series, binary classification, classification,
and information retrieval problems. It has zero dependencies and
a consistent, simple interface for all functions.
|
|
r-cran-mlbench
GNU R Machine Learning Benchmark Problems
|
Versions of package r-cran-mlbench |
Release | Version | Architectures |
sid | 2.1-5-1 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
stretch | 2.1-1-1 | amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x |
bookworm | 2.1-3-1 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
buster | 2.1-1-3 | amd64,arm64,armhf,i386 |
trixie | 2.1-5-1 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
bullseye | 2.1-3-1 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
|
License: DFSG free
|
This GNU R package provices a collection of artificial and real-world
machine learning benchmark problems, including, e.g., several data sets
from the UCI repository.
|
|
r-cran-mlr
Machine learning in GNU R
|
Versions of package r-cran-mlr |
Release | Version | Architectures |
trixie | 2.19.1+dfsg-1 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
bookworm | 2.19.1+dfsg-1 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
bullseye | 2.18.0+dfsg-1 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
buster | 2.13-1 | amd64,arm64,armhf,i386 |
stretch-backports | 2.13-1~bpo9+1 | amd64 |
sid | 2.19.1+dfsg-1 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
upstream | 2.19.2 |
|
License: DFSG free
|
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.
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r-cran-mnp
GNU R package for fitting multinomial probit (MNP) models
|
Versions of package r-cran-mnp |
Release | Version | Architectures |
buster | 3.1-0-2 | amd64,arm64,armhf,i386 |
trixie | 3.1-4-1 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
bookworm | 3.1-3-1 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
sid | 3.1-4-1 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
bullseye | 3.1-1-1 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
jessie | 2.6-4-1 | amd64,armel,armhf,i386 |
stretch | 2.6-4-1 | amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x |
upstream | 3.1-5 |
Debtags of package r-cran-mnp: |
devel | lang:r, library |
field | statistics |
role | app-data |
suite | gnu |
|
License: DFSG free
|
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).
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r-cran-msm
GNU R Multi-state Markov and hidden Markov models in continuous time
|
Versions of package r-cran-msm |
Release | Version | Architectures |
buster | 1.6.6-2 | amd64,arm64,armhf,i386 |
sid | 1.8-1 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
trixie | 1.8-1 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
jessie | 1.4-2 | amd64,armel,armhf,i386 |
bookworm | 1.7-1 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
bullseye | 1.6.8-1 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
stretch | 1.6.4-1 | amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x |
upstream | 1.8.2 |
Debtags of package r-cran-msm: |
interface | commandline |
role | program |
|
License: DFSG free
|
Functions for fitting general continuous-time Markov and hidden Markov
multi-state models to longitudinal data. Both Markov transition rates and the
hidden Markov output process can be modelled in terms of covariates. A variety
of observation schemes are supported, including processes observed at arbitrary
times, completely-observed processes, and censored states.
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r-cran-tgp
GNU R Bayesian treed Gaussian process models
|
Versions of package r-cran-tgp |
Release | Version | Architectures |
jessie | 2.4-9-1 | amd64,armel,armhf,i386 |
buster | 2.4-14-4 | amd64,arm64,armhf,i386 |
trixie | 2.4-23-1 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
sid | 2.4-23-1 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
bullseye | 2.4-17-1 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
stretch | 2.4-14-2 | amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x |
bookworm | 2.4-21-1 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
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License: DFSG free
|
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.
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root-system
??? missing short description for package root-system :-(
|
Versions of package root-system |
Release | Version | Architectures |
jessie | 5.34.19+dfsg-1.2 | all |
Debtags of package root-system: |
field | physics |
|
License: DFSG free
|
|
|
scilab-ann
??? missing short description for package scilab-ann :-(
|
Versions of package scilab-ann |
Release | Version | Architectures |
stretch | 0.4.2.4-1 | all |
jessie | 0.4.2.4-1 | all |
Debtags of package scilab-ann: |
devel | library |
role | devel-lib, shared-lib |
|
License: DFSG free
|
|
|
torch-core-free
Scientific Computing Framework For Luajit (Core Components)
|
Versions of package torch-core-free |
Release | Version | Architectures |
buster | 20171127 | amd64,armhf |
|
License: DFSG free
|
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.
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toulbar2
Exact combinatorial optimization for Graphical Models
|
Versions of package toulbar2 |
Release | Version | Architectures |
trixie | 1.2.1+dfsg-0.1 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
bullseye | 1.1.1+dfsg-1 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
buster | 1.0.0+dfsg3-2 | amd64,arm64,armhf,i386 |
bookworm | 1.1.1+dfsg-1 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
sid | 1.2.1+dfsg-0.1 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
|
License: DFSG free
|
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.
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vowpal-wabbit
??? missing short description for package vowpal-wabbit :-(
|
Versions of package vowpal-wabbit |
Release | Version | Architectures |
jessie | 7.3-1.1 | amd64,armel,armhf,i386 |
Debtags of package vowpal-wabbit: |
interface | commandline |
role | program |
scope | utility |
|
License: DFSG free
|
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weka
Machine learning algorithms for data mining tasks
|
Versions of package weka |
Release | Version | Architectures |
sid | 3.6.14-4 | all |
jessie | 3.6.11-1 | all |
trixie | 3.6.14-4 | all |
bookworm | 3.6.14-3 | all |
bullseye | 3.6.14-2 | all |
stretch | 3.6.14-1 | all |
buster | 3.6.14-1 | all |
upstream | 3.8.6 |
Debtags of package weka: |
field | statistics |
interface | commandline, x11 |
role | program |
science | calculation |
scope | utility |
use | analysing, calculating |
works-with | db, text |
x11 | application |
|
License: DFSG free
|
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.
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yap
??? missing short description for package yap :-(
|
Versions of package yap |
Release | Version | Architectures |
stretch | 6.2.2-6 | amd64,arm64,armel,armhf,i386 |
jessie | 6.2.2-2 | amd64,armel,armhf,i386 |
Debtags of package yap: |
devel | compiler, interpreter, lang:prolog |
role | program |
|
License: DFSG free
|
|
|
Official Debian packages with lower relevance
ask
Adaptive Sampling Kit for big experimental spaces
|
Versions of package ask |
Release | Version | Architectures |
jessie | 1.0.1-2 | all |
buster | 1.1.1-3 | all |
stretch | 1.1.1-1 | all |
|
License: DFSG free
|
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.
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libdlib-dev
C++ toolkit for machine learning and computer vision - development
|
Versions of package libdlib-dev |
Release | Version | Architectures |
buster | 19.10-3 | amd64,arm64,armhf,i386 |
bookworm | 19.24+dfsg-1 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
bullseye | 19.10-3.1 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
trixie | 19.24.6+dfsg-1 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
sid | 19.24.6+dfsg-1 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
stretch | 18.18-2 | amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x |
|
License: DFSG free
|
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.
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libdlpack-dev
Open In Memory Tensor Structure
|
Versions of package libdlpack-dev |
Release | Version | Architectures |
trixie | 1.0-1 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
bullseye | 0.0~git20200217.3ec0443-2 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
bookworm | 0.6-1 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
sid | 1.0-1 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
upstream | 1.0rc |
|
License: DFSG free
|
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.
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libfannj-java
FannJ a Java binding to the Fast Artificial Neural Network (FANN) C library
|
Versions of package libfannj-java |
Release | Version | Architectures |
sid | 0.7-1 | all |
jessie | 0.3-1 | all |
bullseye | 0.3-2 | all |
buster | 0.3-2 | all |
stretch | 0.3-1 | all |
bookworm | 0.7-1 | all |
trixie | 0.7-1 | all |
|
License: DFSG free
|
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.
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libfclib-dev
read and write problems from the Friction Contact Library (headers)
|
Versions of package libfclib-dev |
Release | Version | Architectures |
bookworm | 3.1.0+dfsg-2 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
trixie | 3.1.0+dfsg-3 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
sid | 3.1.0+dfsg-3 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
bullseye | 3.1.0+dfsg-2 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
buster | 3.0.0+dfsg-2 | amd64,arm64,armhf,i386 |
|
License: DFSG free
|
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.
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libmkldnn-dev
Intel Math Kernel Library for Deep Neural Networks (dev)
|
Versions of package libmkldnn-dev |
Release | Version | Architectures |
buster | 0.17.4-1 | amd64 |
|
License: DFSG free
|
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.
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libmrgingham-dev
Chessboard finder for visual calibration routines
|
Versions of package libmrgingham-dev |
Release | Version | Architectures |
sid | 1.24-2 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
bookworm | 1.22-1 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
trixie | 1.24-2 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
|
License: DFSG free
|
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
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libxgboost-predictor-java
Java implementation of XGBoost predictor for online prediction tasks
|
Versions of package libxgboost-predictor-java |
Release | Version | Architectures |
trixie | 0.3.1+dfsg-2 | all |
bookworm | 0.3.1+dfsg-2 | all |
sid | 0.3.1+dfsg-2 | all |
|
License: DFSG free
|
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.
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libxsmm-dev
Library for matrix operations and deep learning primitives
|
Versions of package libxsmm-dev |
Release | Version | Architectures |
sid | 1.17-4 | amd64 |
bookworm | 1.17-2 | amd64 |
trixie | 1.17-4 | amd64 |
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License: DFSG free
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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.
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python3-hdmedians
high-dimensional medians in Python3
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Versions of package python3-hdmedians |
Release | Version | Architectures |
bookworm | 0.14.2-5 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
bullseye | 0.14.1-1 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
trixie | 0.14.2-5.1 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
sid | 0.14.2-5.1 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
buster | 0.13~git20171027.8e0e9e3-1 | amd64,arm64,armhf,i386 |
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License: DFSG free
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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.
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python3-imblearn
library providing resampling techniques
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Versions of package python3-imblearn |
Release | Version | Architectures |
sid | 0.12.4-1 | all |
trixie | 0.12.4-1 | all |
bullseye | 0.7.0-6 | all |
bookworm | 0.10.0-1 | all |
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License: DFSG free
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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.
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python3-liac-arff
library for reading and writing ARFF files in Python
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Versions of package python3-liac-arff |
Release | Version | Architectures |
bookworm | 2.5.0-3 | all |
sid | 2.5.0-6 | all |
trixie | 2.5.0-6 | all |
bullseye | 2.5.0-1 | all |
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License: DFSG free
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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.
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python3-mrgingham
Chessboard finder for visual calibration routines
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Versions of package python3-mrgingham |
Release | Version | Architectures |
bookworm | 1.22-1 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
sid | 1.24-2 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
trixie | 1.24-2 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
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License: DFSG free
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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
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science-numericalcomputation
Debian Science Numerical Computation packages
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Versions of package science-numericalcomputation |
Release | Version | Architectures |
buster | 1.10 | all |
sid | 1.14.7 | all |
bullseye | 1.14.2 | all |
bookworm | 1.14.5 | all |
stretch | 1.7 | all |
trixie | 1.14.7 | all |
jessie | 1.4 | all |
Debtags of package science-numericalcomputation: |
devel | lang:lisp |
role | metapackage, shared-lib |
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License: DFSG free
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This metapackage will install Debian Science packages useful for
numerical computation. The packages provide an array oriented
calculation and visualisation system for scientific computing and
data analysis. These packages are similar to commercial systems such
as Matlab and IDL.
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science-statistics
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Versions of package science-statistics |
Release | Version | Architectures |
trixie | 1.14.7 | all |
buster | 1.10 | all |
bookworm | 1.14.5 | all |
stretch | 1.7 | all |
bullseye | 1.14.2 | all |
sid | 1.14.7 | all |
jessie | 1.4 | all |
Debtags of package science-statistics: |
role | metapackage |
suite | debian |
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License: DFSG free
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本元包是 Debian Pure Blend“Debian Science“(Debian 科学)的一部分,它将引入
与统计相关的软件包。作为一般的任务软件包,其目标是针对科学工作。具体来说,
该包依赖许多 R 软件包,以及其它对统计工作有意义的工具。除此之外,您还可以考
虑安装该包建议安装的 Debian 数学任务包以安装所有与数学相关的软件。
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science-typesetting
Debian Science typesetting packages
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Versions of package science-typesetting |
Release | Version | Architectures |
jessie | 1.4 | all |
sid | 1.14.7 | all |
trixie | 1.14.7 | all |
bookworm | 1.14.5 | all |
bullseye | 1.14.2 | all |
buster | 1.10 | all |
stretch | 1.7 | all |
Debtags of package science-typesetting: |
role | metapackage |
suite | debian |
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License: DFSG free
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This metapackage will install Debian Science packages related to
typesetting. You might also be interested in the use::typesetting
debtag.
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Debian packages in contrib or non-free
caffe-cuda
Fast, open framework for Deep Learning (Meta)
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Versions of package caffe-cuda |
Release | Version | Architectures |
buster | 1.0.0+git20180821.99bd997-2 (contrib) | amd64 |
stretch | 1.0.0~rc4-1 (contrib) | amd64 |
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License: DFSG free, but needs non-free components
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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:
Note, this CUDA version cannot co-exist with the CPU_ONLY version.
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Packaging has started and developers might try the packaging code in VCS
spacy
Industrial-strength Natural Language Processing (NLP)
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Versions of package spacy |
Release | Version | Architectures |
VCS | 2.2.3-1 | all |
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License: MIT
Debian package not available
Version: 2.2.3-1
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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.
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streamlit
fast way to build custom ML tools
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Versions of package streamlit |
Release | Version | Architectures |
VCS | 0.56.0-1 | all |
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License: Apache-2.0
Debian package not available
Version: 0.56.0-1
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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.
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Unofficial packages built by somebody else
python3-orange
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License: GPLv3
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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.
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No known packages available but some record of interest (WNPP bug)
Fast Library for Approximate Nearest Neighbors
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License: BSD
Debian package not available
Language: C++
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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.
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Modular Machine Learning Library
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License: BSD
Debian package not available
Language: Python
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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.
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