Displaying 1 to 20 from 24 results

dist-keras - Distributed Deep Learning, with a focus on distributed training, using Keras and Apache Spark

  •    Python

Distributed Deep Learning with Apache Spark and Keras. Distributed Keras is a distributed deep learning framework built op top of Apache Spark and Keras, with a focus on "state-of-the-art" distributed optimization algorithms. We designed the framework in such a way that a new distributed optimizer could be implemented with ease, thus enabling a person to focus on research. Several distributed methods are supported, such as, but not restricted to, the training of ensembles and models using data parallel methods.

ABAGAIL - The library contains a number of interconnected Java packages that implement machine learning and artificial intelligence algorithms

  •    Java

The library contains a number of interconnected Java packages that implement machine learning and artificial intelligence algorithms. These are artificial intelligence algorithms implemented for the kind of people that like to implement algorithms themselves. See Issues page.

fmin - Unconstrained function minimization in Javascript

  •    Javascript

Unconstrained function minimization in javascript. This package implements some basic numerical optimization algorithms: Nelder-Mead, Gradient Descent, Wolf Line Search and Non-Linear Conjugate Gradient methods are all provided.

ProxASAGA - Proximal Asynchronous SAGA

  •    C++

The code has been tested on OSX and Linux. The C-OPT library contains a pure Python implementation (using Numba) of the sequential algorithm. Note that because Numba lacks atomic types, a pure Python implementation of the parallel algorithm is not straightforward.

pagmo2 - A C++ / Python platform to perform parallel computations of optimisation tasks (global and local) via the asynchronous generalized island model

  •    C++

pagmo (C++) or pygmo (Python) is a scientific library for massively parallel optimization. It is built around the idea of providing a unified interface to optimization algorithms and to optimization problems and to make their deployment in massively parallel environments easy. If you are using pagmo/pygmo as part of your research, teaching, or other activities, we would be grateful if you could star the repository and/or cite our work. The DOI of the latest version and other citation resources are available at this link.

SGDLibrary - MATLAB library for stochastic optimization algorithms: Version 1.0.17

  •    Terra

The SGDLibrary is a pure-MATLAB library of a collection of stochastic optimization algorithms. This solves an unconstrained minimization problem of the form, min f(x) = sum_i f_i(x). The SGDLibrary is also operable on GNU Octave (Free software compatible with many MATLAB scripts). Note that this SGDLibrary internally contains the GDLibrary.

StructuredOptimization.jl - Structured optimization in Julia

  •    Julia

StructuredOptimization.jl is a high-level modeling language that utilizes a syntax that is very close to the mathematical formulation of an optimization problem. StructuredOptimization.jl can handle large-scale convex and nonconvex problems with nonsmooth cost functions.

ForBES - Generic and efficient MATLAB solver for nonsmooth optimization problems

  •    Matlab

ForBES (standing for Forward-Backward Envelope Solver) is a MATLAB solver for nonsmooth optimization problems. It is generic in the sense that the user can customize the problem to solve in an easy and flexible way. It is efficient since it features very efficient algorithms, suited for large scale applications.

viz_torch_optim - Videos of deep learning optimizers moving on 3D problem-landscapes

  •    Jupyter

This project generates animations of pytorch optimizers solving toy problems. Examples Below. Some nice animations were posted a few years ago by Alex Radford but didn't include the Adam optimizer or landscapes with noise. Louis Tiao blogged about how to make the visualizations. The pytorch unit tests show how to run the optimizers on test functions. I pulled these together and shared the result at https://github.com/wassname/viz_torch_optim.

FewShotLearning - Pytorch implementation of the paper "Optimization as a Model for Few-Shot Learning"

  •    Python

This repo provides a Pytorch implementation for the Optimization as a Model for Few-Shot Learning paper.

cilib - Typesafe, purely functional Computational Intelligence

  •    Scala

CIlib is a library of various computational intelligence algorithms. The goal of the project is to create a library that can be used and referenced by individuals and researchers alike. CIlib is not a "framework", instead the library is a set of a few very simple abstractions, and allows for a principled manner to define computational intelligence algorithms and uses several typeclasses such as Functor and Monad.

FirstOrderSolvers.jl - Large scale convex optimization solvers in julia

  •    Julia

Package for large scale convex optimization solvers in julia. This package is intended to allow for easy implementation, testing, and running of solvers through the Convex.jl interface. The package is currently under active development and uses the ProximalOperators.jl package to do the low level projections. These values correspond to the values in the paper Conic Optimization via Operator Splitting and Homogeneous Self-Dual Embedding (O'Donoghue et.al).

minSQN - Optimization using Stochastic quasi-Newton methods

  •    Matlab

Please contact us if you have any questions, suggestions, requests or bug-reports. This is a package for solving an unconstrained minimization problem of the form, min f(x) = (1/n)*sum_i f_i(x).

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