bayadera - High-performance Bayesian Data Analysis on the GPU in Clojure

  •        22

A Clojure Library for Bayesian Data Analysis and Machine Learning on the GPU. Distributed under the Eclipse Public License either version 1.0 or (at your option) any later version.



Related Projects

PyMC3 - Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Theano

  •    Python

PyMC3 is a Python package for Bayesian statistical modeling and Probabilistic Machine Learning which focuses on advanced Markov chain Monte Carlo and variational fitting algorithms. Its flexibility and extensibility make it applicable to a large suite of problems.Note: Running pip install pymc will install PyMC 2.3, not PyMC3, from PyPI.

Arraymancer - A fast, ergonomic and portable tensor library in Nim with a deep learning focus for CPU, GPU, OpenCL and embedded devices

  •    Nim

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.

edward - A probabilistic programming language in TensorFlow

  •    Jupyter

Edward is a Python library for probabilistic modeling, inference, and criticism. It is a testbed for fast experimentation and research with probabilistic models, ranging from classical hierarchical models on small data sets to complex deep probabilistic models on large data sets. Edward fuses three fields: Bayesian statistics and machine learning, deep learning, and probabilistic programming. Edward is built on top of TensorFlow. It enables features such as computational graphs, distributed training, CPU/GPU integration, automatic differentiation, and visualization with TensorBoard.

fastFM - fastFM: A Library for Factorization Machines

  •    Python

The library fastFM is an academic project. The time and resources spent developing fastFM are therefore justified by the number of citations of the software. If you publish scientific articles using fastFM, please cite the following article (bibtex entry citation.bib). This repository allows you to use Factorization Machines in Python (2.7 & 3.x) with the well known scikit-learn API. All performance critical code as been written in C and wrapped with Cython. fastFM provides stochastic gradient descent (SGD) and coordinate descent (CD) optimization routines as well as Markov Chain Monte Carlo (MCMC) for Bayesian inference. The solvers can be used for regression, classification and ranking problems. Detailed usage instructions can be found in the online documentation and on arXiv.

neanderthal - Fast Clojure Matrix Library

  •    Clojure

Neanderthal is a Clojure library for fast matrix and linear algebra computations based on the highly optimized native libraries of BLAS and LAPACK computation routines for both CPU and GPU.. Read the documentation at Neanderthal Web Site.

Probabilistic-Programming-and-Bayesian-Methods-for-Hackers - aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view

  •    Jupyter

The Bayesian method is the natural approach to inference, yet it is hidden from readers behind chapters of slow, mathematical analysis. The typical text on Bayesian inference involves two to three chapters on probability theory, then enters what Bayesian inference is. Unfortunately, due to mathematical intractability of most Bayesian models, the reader is only shown simple, artificial examples. This can leave the user with a so-what feeling about Bayesian inference. In fact, this was the author's own prior opinion. After some recent success of Bayesian methods in machine-learning competitions, I decided to investigate the subject again. Even with my mathematical background, it took me three straight-days of reading examples and trying to put the pieces together to understand the methods. There was simply not enough literature bridging theory to practice. The problem with my misunderstanding was the disconnect between Bayesian mathematics and probabilistic programming. That being said, I suffered then so the reader would not have to now. This book attempts to bridge the gap.

gunrock - High-Performance Graph Primitives on GPUs

  •    Cuda

Gunrock is a CUDA library for graph-processing designed specifically for the GPU. It uses a high-level, bulk-synchronous, data-centric abstraction focused on operations on a vertex or edge frontier. Gunrock achieves a balance between performance and expressiveness by coupling high performance GPU computing primitives and optimization strategies with a high-level programming model that allows programmers to quickly develop new graph primitives with small code size and minimal GPU programming knowledge. For more details, please visit our website, read Why Gunrock, our TOPC 2017 paper Gunrock: GPU Graph Analytics, look at our results, and find more details in our publications. See Release Notes to keep up with the our latest changes.

emu - a language for programming GPUs, with a focus on ergonomics first and performance second

  •    Rust

⚠ Please note that while Emu 0.2.0 is quite usable, it suffers from 2 key issues. It firstly does nothing to minimize CPU-GPU data transfer and secondly it's compiler is not well-tested. These can be reasons not to use Emu 0.2.0. A new version of Emu is in the works, however, with significant improvements in the language, compiler, and compile-time checker. This new version of Emu should be released some time in Q4 of 2019. But unlike OpenCL/CUDA/Halide/Futhark, Emu is embedded in Rust. This lets it take advantage of the ecosystem in ways...

Bayesian Phylogenetic Diagnostics


This project will focus on the development of tools and analyses for the diagnosis of convergence in Markov Chain Monte Carlo (MCMC) processes, with a main focus on inference of phylogeny using Bayesian statistical methods.

Bios8366 - Advanced Statistical Computing at Vanderbilt University's Department of Biostatistics

  •    Jupyter

Course covers numerical optimization, statistical machine learning, Markov Chain Monte Carlo (MCMC), variational inference (VI) algorithms, data augmentation algorithms with applications for model fitting and techniques for dealing with missing data. Prerequisites: Bios 6341 (Fundamentals of Probability), Bios 6342 (Contemporary Statistical Inference), or permission of instructor. Students must be familiar with basic probability, have some formal programming experience, and be comfortable using the Git version control system.

tf-quant-finance - High-performance TensorFlow library for quantitative finance.

  •    Jupyter

This library provides high-performance components leveraging the hardware acceleration support and automatic differentiation of TensorFlow. The library will provide TensorFlow support for foundational mathematical methods, mid-level methods, and specific pricing models. The coverage is being rapidly expanded over the next few months. Foundational methods. Core mathematical methods - optimisation, interpolation, root finders, linear algebra, random and quasi-random number generation, etc.

compute - A C++ GPU Computing Library for OpenCL

  •    C++

Boost.Compute is a GPU/parallel-computing library for C++ based on OpenCL. The core library is a thin C++ wrapper over the OpenCL API and provides access to compute devices, contexts, command queues and memory buffers.

probability - Probabilistic reasoning and statistical analysis in TensorFlow

  •    Jupyter

TensorFlow Probability is a library for probabilistic reasoning and statistical analysis in TensorFlow. As part of the TensorFlow ecosystem, TensorFlow Probability provides integration of probabilistic methods with deep networks, gradient-based inference via automatic differentiation, and scalability to large datasets and models via hardware acceleration (e.g., GPUs) and distributed computation. Our probabilistic machine learning tools are structured as follows.

BDA_py_demos - Bayesian Data Analysis demos for Python

  •    Jupyter

to interactively run the IPython Notebooks in the browser. This repository contains some Python demos for the book Bayesian Data Analysis, 3rd ed by Gelman, Carlin, Stern, Dunson, Vehtari, and Rubin (BDA3).

Accord.NET - Machine learning, Computer vision, Statistics and general scientific computing for .NET

  •    CSharp

The Accord.NET project provides machine learning, statistics, artificial intelligence, computer vision and image processing methods to .NET. It can be used on Microsoft Windows, Xamarin, Unity3D, Windows Store applications, Linux or mobile.

ROCm - ROCm - Open Source Platform for HPC and Ultrascale GPU Computing


The ROCm Platform brings a rich foundation to advanced computing by seamlessly integrating the CPU and GPU with the goal of solving real-world problems. This software enables the high-performance operation of AMD GPUs for computationally oriented tasks in the Linux operating system. ROCm is focused on using AMD GPUs to accelerate computational tasks such as machine learning, engineering workloads, and scientific computing. In order to focus our development efforts on these domains of interest, ROCm supports a targeted set of hardware configurations which are detailed further in this section.

qcgpu-rust - A High Performance, Hardware accelerated, Quantum computer simulator in Rust

  •    Rust

The goal of QCGPU is to provide a library for the simulation of quantum computers that is fast, efficient and portable. QCGPU is written in Rust and uses OpenCL to run code on the CPU, GPU or any other OpenCL supported devices. This library is meant to be used both independently and alongside established tools for example compilers or more general and high level frameworks. If you are interested in using QCGPU with IBM's QISKit framework or QISKit ACQUA, please see the repository qiskit-addon-qcgpu.

rstan - RStan, the R interface to Stan

  •    R

RStan is the R interface to Stan. RStan's source code repository is hosted here on GitHub. Stan's source repository is defined as a submodule. See how to work with stan submodule in rstan repo.

mshadow - Matrix Shadow:Lightweight CPU/GPU Matrix and Tensor Template Library in C++/CUDA for (Deep) Machine Learning

  •    C++

MShadow is a lightweight CPU/GPU Matrix/Tensor Template Library in C++/CUDA. The goal of mshadow is to support efficient, device invariant and simple tensor library for machine learning project that aims for maximum performance and control, while also emphasize simplicity.MShadow also provides interface that allows writing Multi-GPU and distributed deep learning programs in an easy and unified way.

Pyro - Deep universal probabilistic programming with Python and PyTorch

  •    Python

Pyro is a universal probabilistic programming language (PPL) written in Python and supported by PyTorch on the backend. Pyro enables flexible and expressive deep probabilistic modeling, unifying the best of modern deep learning and Bayesian modeling.

We have large collection of open source products. Follow the tags from Tag Cloud >>

Open source products are scattered around the web. Please provide information about the open source projects you own / you use. Add Projects.