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PyTorch is a deep learning framework that puts Python first. It is a python package that provides Tensor computation (like numpy) with strong GPU acceleration, 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.

neural-network autograd gpu numpy deep-learning tensorTVM is a Tensor intermediate representation(IR) stack for deep learning systems. It is designed to close the gap between the productivity-focused deep learning frameworks, and the performance- and efficiency-focused hardware backends. TVM works with deep learning frameworks to provide end to end compilation to different backends. Checkout our announcement for more details.© Contributors, 2017. Licensed under an Apache-2.0 license.

compiler tensor deep-learning dsl gpu opencl metal performance rocm tvmBesides its obvious scientific uses, NumJs can also be used as an efficient multi-dimensional container of generic data. NumJs is licensed under the MIT license, enabling reuse with almost no restrictions.

linear-algebra ndarray nodejs array multi multidimensional dimension higher image volume webgl tensor matrix linear algebra science numerical computing stride shape numpyPyTorch tutorials and fun projects including neural talk, neural style, poem writing, anime generation

pytorch pytorch-tutorials pytorch-tutorials-cn deep-learning neural-style charrnn gan caption neuraltalk image-classification visdom tensorboard nn tensor autograd jupyter-notebookLibRec (http://www.librec.net) is a Java library for recommender systems (Java version 1.7 or higher required). It implements a suit of state-of-the-art recommendation algorithms, aiming to resolve two classic recommendation tasks: rating prediction and item ranking. A movie recommender system is designed and available here.

recommender-systems recommendation-algorithms collaborative-filtering matrix-factorization tensor-factorization probabilistic-graphical-models recommender systems factorization matrix tensor collaborative filtering sparseModular multidimensional arrays for JavaScript.ndarrays can be transposed, flipped, sheared and sliced in constant time per operation. They are useful for representing images, audio, volume graphics, matrices, strings and much more. They work both in node.js and with browserify.

ndarray array multi multidimensional dimension higher image volume webgl tensor matrix linear algebra science numerical computing stride shapeTensorFlow is Google's machine learning runtime. It is implemented as C++ runtime, along with Python framework to support building a variety of models, especially neural networks for deep learning. It is interesting to be able to use TensorFlow in a node.js application using just JavaScript (or TypeScript if that's your preference). However, the Python functionality is vast (several ops, estimator implementations etc.) and continually expanding. Instead, it would be more practical to consider building Graphs and training models in Python, and then consuming those for runtime use-cases (like prediction or inference) in a pure node.js and Python-free deployment. This is what this node module enables.

tensorflow node-tensorflow nodejs machine-learning deep-learning npm-package tf tensor ml ai neural-networks neuralnetworks deeplearning model numerical-computation googleEgison is the pattern-matching-oriented purely functional programming language. We can directly represent pattern-matching against lists, multisets, sets, trees, graphs and any kind of data types. This is the repository of the interpreter of Egison. For more information, visit our website.

egison programming-language functional-programming pattern-matching computer-algebra-system tensor differential-geometryLow-Rank and Sparse tools for Background Modeling and Subtraction in Videos. The LRSLibrary provides a collection of low-rank and sparse decomposition algorithms in MATLAB. The library was designed for motion segmentation in videos, but it can be also used (or adapted) for other computer vision problems (for more information, please see this page). Currently the LRSLibrary offers more than 100 algorithms based on matrix and tensor methods. The LRSLibrary was tested successfully in several MATLAB versions (e.g. R2014, R2015, R2016, R2017, on both x86 and x64 versions). It requires minimum R2014b.

rpca matrix-factorization matrix-completion tensor-decomposition tensor matlab matrix subspace-tracking subspace-learningArraymancer is a tensor (N-dimensional array) project in Nim. The main focus is providing a fast and ergonomic CPU, Cuda and OpenCL ndarray library on which to build a scientific computing and in particular a deep learning ecosystem. The library is inspired by Numpy and PyTorch. The library provides ergonomics very similar to Numpy, Julia and Matlab but is fully parallel and significantly faster than those libraries. It is also faster than C-based Torch.

tensor nim multidimensional-arrays cuda deep-learning machine-learning cudnn high-performance-computing gpu-computing matrix-library neural-networks parallel-computing openmp linear-algebra ndarray opencl gpgpu iot automatic-differentiation autogradNew: checkout matrices and vectors made of strings, with cyclic algebra.NOTA BENE Imagine all code examples below as written in some REPL where expected output is documented as a comment.

algebra tensor vector matrix real complex quaternion mathWe do not intend to implement of Tensor and Ops, but instead use this as common bridge to reuse tensor and ops across frameworks.RFC proposals are opened as issues. The major release will happen as a vote issue to make sure the participants agree on the changes.

tensor operator deep-learningNOTE: PyTorch is evolving rapidly. With the advent of tracing during execution and the upcoming GraphExecutor in ATen, that will be the way to run computation graphs in C++. A Python module for compiling (static) PyTorch graphs to C (relying on TH and THNN).

pytorch graph tensor compiled-graphs deep-learningAIscm is a Guile extension for numerical arrays and tensors. Performance is achieved by means of a JIT compiler.

guile numeric-arrays tensor debianNormally Furious.js would automatically detect the optimal backend, but it is possible to specify it manually. If you plan to use Node-WebCL, you'll need to install the upstream version of Node-WebCL, and its dependencies.

ndarray array matrix tensor hpc computing blas science scientific numeric math mathematics statisticsnodejs bindings for libTH (tensor library that powers torch). for fun!

torch ffi bindings tensorSPLATT is a library and C API for sparse tensor factorization. SPLATT supports shared-memory parallelism with OpenMP and distributed-memory parallelism with MPI. will suffice. The installation prefix can be chosen by adding a '--prefix=DIR' flag to configure.

tensor parallel machine-learning cpd mpi openmpThe freeCappuccino is a three-dimensional fully unstructured finite volume code for Computational Fluid Dynamics which comes in serial and parallel version. Moreover, freeCappuccino is a fortran library for manipulation of discrete tensor fields, defined over polyhedral meshes.

cfd simulation linear-solvers turbulence tensor fluid-dynamics mpi numerical-methods finite-volumeEfficient computations with symmetric and non-symmetric tensors with support for automatic differentiation. This Julia package provides fast operations with symmetric and non-symmetric tensors of order 1, 2 and 4. The Tensors are allocated on the stack which means that there is no need to preallocate output results for performance. Unicode infix operators are provided such that the tensor expression in the source code is similar to the one written with mathematical notation. When possible, symmetry of tensors is exploited for better performance. Supports Automatic Differentiation to easily compute first and second order derivatives of tensorial functions.

computational-mechanics tensor femEfficient computations with symmetric and non-symmetric tensors with support for automatic differentiation. This Julia package provides fast operations with symmetric and non-symmetric tensors of order 1, 2 and 4. The Tensors are allocated on the stack which means that there is no need to preallocate output results for performance. Unicode infix operators are provided such that the tensor expression in the source code is similar to the one written with mathematical notation. When possible, symmetry of tensors is exploited for better performance. Supports Automatic Differentiation to easily compute first and second order derivatives of tensorial functions.

finite-elements cfd tensor symmetric-tensors automatic-differentiation
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