Search Results for "e-learning"
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Showing 155 open source projects for "e-learning"
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PyTorch Geometric
Geometric deep learning extension library for PyTorch
It consists of various methods for deep learning on graphs and other irregular structures, also known as geometric deep learning, from a variety of published papers. In addition, it consists of an easy-to-use mini-batch loader for many small and single giant graphs, a large number of common benchmark datasets (based on simple interfaces to create your own), and helpful transforms, both for learning on arbitrary graphs as well as on 3D meshes or point clouds. -
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Mathematics Dataset
This dataset code generates mathematical question and answer pairs
...The dataset enables models to learn mathematical problem-solving through examples that involve both numeric and symbolic reasoning. Version 1.0 includes over 2 million examples per category, with training splits labeled as “easy,” “medium,” and “hard,” supporting curriculum-based learning strategies. The data can be accessed via PyPI or generated locally using provided Python scripts, with outputs formatted for direct use in training or evaluation pipelines. -
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PyTensor
Python library for defining and optimizing mathematical expressions
PyTensor is a fork of Aesara, a Python library for defining, optimizing, and efficiently evaluating mathematical expressions involving multi-dimensional arrays. PyTensor is based on Theano, which has been powering large-scale computationally intensive scientific investigations since 2007. A hackable, pure-Python codebase. Extensible graph framework is suitable for rapid development of custom operators and symbolic optimizations. Implements an extensible graph transpilation framework that... -
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alphageometry
AI-driven neuro-symbolic solver for high-school geometry problems
AlphaGeometry, developed by Google DeepMind, is a theorem-proving system that combines symbolic reasoning with deep learning to solve challenging geometry problems, such as those found in mathematical Olympiads. The repository provides the full implementation of DDAR (Deductive Difference and Abductive Reasoning) and AlphaGeometry, two automated geometry solvers described in the 2024 Nature paper “Solving Olympiad Geometry without Human Demonstrations.” -
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CUTLASS
CUDA Templates for Linear Algebra Subroutines
CUTLASS is a collection of CUDA C++ template abstractions for implementing high-performance matrix-multiplication (GEMM) and related computations at all levels and scales within CUDA. It incorporates strategies for hierarchical decomposition and data movement similar to those used to implement cuBLAS and cuDNN. CUTLASS decomposes these "moving parts" into reusable, modular software components abstracted by C++ template classes. These thread-wide, warp-wide, block-wide, and device-wide... -
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PyMC
Bayesian Modeling and Probabilistic Programming in Python
PyMC is a Python library for probabilistic programming focused on Bayesian statistical modeling and machine learning. Built on top of computational tools like Aesara and NumPy, PyMC allows users to define models using intuitive syntax and perform inference using MCMC, variational inference, and other advanced algorithms. It’s widely used in scientific research, data science, and decision modeling. -
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Algebraic graphs
Algebraic graphs
...See this Haskell Symposium paper and the corresponding talk for the motivation behind the library, the underlying theory and implementation details. We can give semantics to the constructors in terms of the pair (V, E) of graph vertices and edges. Alga can handle graphs comprising millions of vertices and billions of edges in a matter of seconds, which is fast enough for many applications. We believe there is a lot of potential for improving the performance of the library, and this is one of our top priorities. If you come across a performance issue when using the library, please let us know. -
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Swift Numerics
Advanced mathematical types and functions for Swift
...The modules are factored to keep dependencies minimal and to allow adopters to pull in only what they need. As a result, Swift Numerics underpins higher-level libraries in simulation, signal processing, and machine learning written in pure Swift. -
9
JAX
Composable transformations of Python+NumPy programs
With its updated version of Autograd, JAX can automatically differentiate native Python and NumPy functions. It can differentiate through loops, branches, recursion, and closures, and it can take derivatives of derivatives of derivatives. It supports reverse-mode differentiation (a.k.a. backpropagation) via grad as well as forward-mode differentiation, and the two can be composed arbitrarily to any order. What’s new is that JAX uses XLA to compile and run your NumPy programs on GPUs and... -
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Armadillo
fast C++ library for linear algebra & scientific computing
* Fast C++ library for linear algebra (matrix maths) and scientific computing * Easy to use functions and syntax, deliberately similar to Matlab / Octave * Uses template meta-programming techniques to increase efficiency * Provides user-friendly wrappers for OpenBLAS, Intel MKL, LAPACK, ATLAS, ARPACK, SuperLU and FFTW libraries * Useful for machine learning, pattern recognition, signal processing, bioinformatics, statistics, finance, etc. * Downloads: http://arma.sourceforge.net/download.html * Documentation: http://arma.sourceforge.net/docs.html * Bug reports: http://arma.sourceforge.net/faq.html * Git repo: https://gitlab.com/conradsnicta/armadillo-code -
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UnBBayes
Framework & GUI for Bayes Nets and other probabilistic models.
UnBBayes is a probabilistic network framework written in Java. It has both a GUI and an API with inference, sampling, learning and evaluation. It supports Bayesian networks, influence diagrams, MSBN, OOBN, HBN, MEBN/PR-OWL, PRM, structure, parameter and incremental learning. Please, visit our wiki (https://sourceforge.net/p/unbbayes/wiki/Home/) for more information. Check out the license section (https://sourceforge.net/p/unbbayes/wiki/License/) for our licensing policy. -
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Bandicoot
fast C++ library for GPU linear algebra & scientific computing
* Fast GPU linear algebra library (matrix maths) for the C++ language, aiming towards a good balance between speed and ease of use * Provides high-level syntax and functionality deliberately similar to Matlab * Provides an API that is aiming to be compatible with Armadillo for easy transition between CPU and GPU linear algebra code * Useful for algorithm development directly in C++, or quick conversion of research code into production environments * Distributed under the permissive Apache 2.0 license, useful for both open-source and proprietary (closed-source) software * Can be used for machine learning, pattern recognition, computer vision, signal processing, bioinformatics, statistics, finance, etc * Downloads: http://coot.sourceforge.io/download.html * Documentation: http://coot.sourceforge.io/docs.html * Bug reports: http://coot.sourceforge.io/faq.html * Git repo: https://gitlab.com/conradsnicta/bandicoot-code -
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antoniovandre_maxima_tools
Tools for Maxima software.
Um conjunto de funções, variáveis e constantes para um uso mais produtivo e sofisticado do software matemático Maxima. -
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Uranie
Uranie is CEA's uncertainty analysis platform, based on ROOT
Uranie is a sensitivity and uncertainty analysis plateform based on the ROOT framework (http://root.cern.ch) . It is developed at CEA, the French Atomic Energy Commission (http://www.cea.fr). It provides various tools for: - data analysis - sampling - statistical modeling - optimisation - sensitivity analysis - uncertainty analysis - running code on high performance computers - etc. Thanks to ROOT, it is easily scriptable in CINT (c++ like syntax) and Python. Is is... -
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Wcalc
A powerful arbitrary-precision calculator.
Wcalc is a powerful arbitrary-precision calculator. It has standard functions (sin, asinh, logtwo, floor, etc), many pre-defined constants (pi, e, c, etc.), variables, "active" variables, command history, and hex/octal/binary i/o, conversions, and more -
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dibnn
Drop In the Bucket Neural Networks
One more lightweight neural network in C. -
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stkpp
C++ Statistical ToolKit
STK++ (http://www.stkpp.org) is a versatile, fast, reliable and elegant collection of C++ classes for statistics, clustering, linear algebra, arrays (with an Eigen-like API), regression, dimension reduction, etc. Some functionalities provided by the library are available in the R environment as R functions (http://cran.at.r-project.org/web/packages/rtkore/index.html). At a convenience, we propose the source packages on sourceforge. The library offers a dense set of (mostly) template... -
18
NZMATH
Python Calculator on Number Theory, three-birds-one learning material
...The "notebook" is available together. It is designed for beginning students of algorithmic number theory to self-study Number Theory, Programming and scientific English together, three-birds-one learning material. It is possible only by running and reading the programs. You can get the notebook here: https://sourceforge.net/projects/nzmath/files/nzmath-enttakagi/ Visit our home page in detail: https://nzmath.sourceforge.io/ -
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MathematicalRamblingsjs
Biblioteca JavaScript Mathematical Ramblings.
Biblioteca de funções, procedimentos e constantes JavaScript do projeto Mathematical Ramblings (https://antoniovandre.github.io/MathematicalRamblingsjs). -
20
STK
a Small (Matlab/Octave) Toolbox for Kriging
...Even though it is, currently, mostly geared towards the Design and Analysis of Computer Experiments (DACE), the STK can be useful for other applications areas (such as Geostatistics, Machine Learning, Non-parametric Regression, etc.). -
21
DataPrep
Python-based data preprocessing tool
DataPrep v0.2 is a Tkinter-based GUI application/tool designed to assist users in data preprocessing, multicollinearity removal, and feature selection for a wide range of applications in Cheminformatics, Bioinformatics, Data Analysis, Feature Selection, Molecular Modeling, Machine Learning, and Quantitative-structure-property relationship (QSPR) studies. It includes functionality to load, process, and save datasets with support for different preprocessing & multicollinearity removal strategies with customizable parameter setting options. -
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nars2000
NARS2000: An Experimental APL Interpreter
APL is an arrary-oriented language initially conceived in the 1960s by Kenneth E. Iverson, IBM Fellow and 1979 ACM Turing Award recipient. It is ideally suited to computer programming because it subsumes the tedious aspects of the job allowing the programmer to concentrate on the heart of the project. NARS2000 is a Free Open Source implementation of APL. For more details on this project, visit the NARS2000.org website. -
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Advanced Trigonometry Calculator
Precision Trigonometry: Advanced Calculator for Complex Math
Advanced Trigonometry Calculator is equipped with a user-friendly interface that allows for easy input of problems and instant computation. Professionals such as engineers who need to perform advanced trigonometric calculations in their work will find this tool extremely useful. ATC Online Alpha: https://advantrigoncalc.sourceforge.io/atc/ More info by clicking below: https://advantrigoncalc.sourceforge.io/ Advanced Trigonometry Calculator was only and always only developed by... -
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Book-Reviews-In-Mathematica
Partial Differential Equations, Complex Analysis, Mathematica, Farlow
Cliff* Notes, Mathematica Evaluatable "Partial Differential Equations for Scientists and Engineers", Farlow "Physics for Scientists and Engineers", Serway "A First Course in Complex Analysis", Beck "PDE", Asmar "PDE, An Introduction", Colton "Elementary Differential Equations", 7th, Rainville "Ordinary Differential Equations", Tenenbaum "Linear Algebra And It's Applications", Lay "Swokowski Calculus", 5th, Swokowski "Chemistry Concepts & Problems, A Self-Teaching Guide",... -
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basic_numerical_methods
Didactic application to aid students in learning Numerical Methods;
A practical tool (for students and engineers) to foresee the result of calculus exercises. Calculation and visualization numerical methods for nonlinear equation, ODE, integration, linear system, polynomial fitting,.....
