Debian Science Machine Learning packages

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

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

This metapackage pulls CUDA version of caffe:

• caffe-tools-cuda
• libcaffe-cuda*

• python3-caffe-cuda And suggests these packages:

• libcaffe-cuda-dev

• caffe-doc

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

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

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

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

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

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

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

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

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

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

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

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

This package provides development files of TMVA package for ROOT.

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

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

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

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

This package contains the modules for Python 3.

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

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

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

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

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

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

• Only layered feedforward networks are supported directly at the moment (for others use the "hooks" provided)
• Unlimited number of layers
• Unlimited number of neurons per each layer separately
• User defined activation function (defaults to logistic)
• User defined error function (defaults to SSE)
• Algorithms implemented so far:
• standard (vanilla) with or without bias, on-line or batch
• momentum with or without bias, on-line or batch
• SuperSAB with or without bias, on-line or batch
• Conjugate gradients
• Jacobian computation
• Computation of result of multiplication between "vector" and Hessian
• Some helper functions provided

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

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

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

A summary of core features:

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

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

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

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

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

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

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

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

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