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PyTorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
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 tensornumjs - Like NumPy, in JavaScript
Besides 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-book - PyTorch tutorials and fun projects including neural talk, neural style, poem writing, anime generation
PyTorch 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 - LibRec: A Leading Java Library for Recommender Systems, see
LibRec (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 sparsetensorly - TensorLy: Tensor Learning in Python.
TensorLy is a Python library that aims at making tensor learning simple and accessible. It allows to easily perform tensor decomposition, tensor learning and tensor algebra. Its backend system allows to seamlessly perform computation with NumPy, PyTorch, JAX, MXNet, TensorFlow or CuPy, and run methods at scale on CPU or GPU. The only pre-requisite is to have Python 3 installed. The easiest way is via the Anaconda distribution.
machine-learning mxnet tensorflow numpy pytorch decomposition tensor-factorization tensor tensor-algebra tensorly tensor-learning tensor-decomposition cupy tensor-regressions tensor-methods jaxTVM - Open deep learning compiler stack for cpu, gpu and specialized accelerators
Apache TVM, a deep learning compiler that enables access to high-performance machine learning anywhere for everyone. TVM’s diverse community of hardware vendors, compiler engineers and ML researchers work together to build a unified, programmable software stack, that enriches the entire ML technology ecosystem and make it accessible to the wider ML community. TVM empowers users to leverage community-driven ML-based optimizations to push the limits and amplify the reach of their research and development, which in turn raises the collective performance of all ML, while driving its costs down.
machine-learning performance deep-learning metal compiler gpu vulkan opencl tensor spirv rocmmars - Mars is a tensor-based unified framework for large-scale data computation which scales numpy, pandas, scikit-learn and Python functions
Mars is a tensor-based unified framework for large-scale data computation which scales numpy, pandas, scikit-learn and many other libraries. More details about installing Mars can be found at installation section in Mars document.
machine-learning tensorflow numpy scikit-learn pandas pytorch xgboost lightgbm tensor dask ray dataframe statsmodels joblibndarray - 📈 Multidimensional arrays for JavaScript
Modular 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 shapenode-tensorflow - Node.js + TensorFlow
TensorFlow 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 - Egison is a purely functional programming language with non-linear pattern-matching against non-free data types
Egison 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-geometrylrslibrary - Low-Rank and Sparse Tools for Background Modeling and Subtraction in Videos
Low-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 - A fast, ergonomic and portable tensor library in Nim with a deep learning focus for CPU, GPU, OpenCL and embedded devices
Arraymancer 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 autogradTenSEAL - A library for doing homomorphic encryption operations on tensors
TenSEAL is a library for doing homomorphic encryption operations on tensors, built on top of Microsoft SEAL. It provides ease of use through a Python API, while preserving efficiency by implementing most of its operations using C++. This installs the last packaged version on pypi. If your platform doesn't have a ready package, please open an issue to let us know.
cryptography encryption deep-learning cpp docker-image tensor homomorphic-encryption hacktoberfest microsoft-sealxtensor-python - Python bindings for xtensor
Python bindings for the xtensor C++ multi-dimensional array library. xtensor is a C++ library for multi-dimensional arrays enabling numpy-style broadcasting and lazy computing.
c-plus-plus python-bindings tensor numpy-arraysalgebra - Vectors, matrices, tensors for Node.js
New: 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 mathdlpack - RFC for common in-memory tensor structure and operator interface for deep learning system
We 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-learningpytorch2c - A Python module for compiling PyTorch graphs to C
NOTE: 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 - Guile numerical arrays and tensor extension
AIscm is a Guile extension for numerical arrays and tensors. Performance is achieved by means of a JIT compiler.
guile numeric-arrays tensor debianfurious.js - scientific computing package for JavaScript - inspired by NumPy
Normally 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 statisticstorch.js - nodejs bindings for libTH (tensor library that powers torch). for fun!
nodejs bindings for libTH (tensor library that powers torch). for fun!
torch ffi bindings tensor
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