Search Results for "optimization" - Page 4
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Showing 93 open source projects for "optimization"
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Functional, Data Science Intro To Python
[tutorial]A functional, Data Science focused introduction to Python
...The assumption is a someone with zero experience in programming can follow this tutorial and learn Python with the smallest amount of information possible. The sections after that, involve varying levels of difficulty and cover topics as diverse as Machine Learning, Linear Optimization, build systems, command line tools, recommendation engines, Sentiment Analysis and Cloud Computing. -
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QuoJS
Micro #JavaScript Library for Mobile Devices
QuoJS is a lightweight JavaScript library aimed at building mobile-first web interfaces with a focus on touch interactions and simple DOM utilities. It provides a compact, jQuery-like API for element selection, traversal, and manipulation, but trims the surface area to keep payloads small for mobile browsers. A core feature set centers on high-level touch gestures—such as tap, double-tap, swipe, pinch, and long-tap—abstracting away platform quirks so developers can attach handlers... -
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GradlePluginDevelop
Gradle execution process
...It contains sample plugin code, project structure, examples of applying plugin logic, extension configuration, and build integration for Gradle plugin development. Gradle execution process, what is DSL, domain-specific language, common usage of Gradle, usage of Gradle advanced plug-ins, Gradle optimization for Android, using Javassist to the next floor, and problems encountered in Gradle development. Demonstration of plugin application in other modules. Plugin configuration and metadata. -
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Compare GAN
Compare GAN code
compare_gan is a research codebase that standardizes how Generative Adversarial Networks are trained and evaluated so results are comparable across papers and datasets. It offers reference implementations for popular GAN architectures and losses, plus a consistent training harness to remove confounding differences in optimization or preprocessing. The library’s evaluation suite includes widely used metrics and diagnostics that quantify sample quality, diversity, and mode coverage. With configuration-driven experiments, you can sweep hyperparameters, run ablations, and log results at scale. The goal is to turn GAN experimentation into a disciplined, repeatable process rather than a patchwork of scripts. ... -
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OptFrame is a framework for efficient implementation of metaheuristics and optimization methods. It has already been used in some real combinatorial problems and applied to Operations Research. Since November 2017, project has been moved to GitHub (new releases will also be included here in SourceForge, but Git mainline is no longer supported here). For more information, visit: https://github.com/OptFrame/optframe
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Solid Python
A comprehensive gradient-free optimization framework written in Python
Solid is a Python framework for gradient-free optimization. It contains basic versions of many of the most common optimization algorithms that do not require the calculation of gradients, and allows for very rapid development using them. -
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fast-neural-style
Feedforward style transfer
...It uses convolutional neural networks to apply artistic styles to images, enabling users to transform photos into stylized outputs inspired by famous artworks. Unlike earlier approaches that required expensive optimization per image, this project leverages feed-forward networks to achieve fast inference, making style transfer practical for real-world applications. The repository includes training scripts, pre-trained models, and examples demonstrating how to apply styles efficiently. It also provides insights into the underlying techniques used in neural style transfer, making it both a practical tool and a learning resource. ... -
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node2vec
Learn continuous vector embeddings for nodes in a graph using biased R
The node2vec project provides an implementation of the node2vec algorithm, a scalable feature learning method for networks. The algorithm is designed to learn continuous vector representations of nodes in a graph by simulating biased random walks and applying skip-gram models from natural language processing. These embeddings capture community structure as well as structural equivalence, enabling machine learning on graphs for tasks such as classification, clustering, and link prediction.... -
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CRFasRNN
Semantic image segmentation method described in the ICCV 2015 paper
CRF-RNN is a deep neural architecture that integrates fully connected Conditional Random Fields (CRFs) with Convolutional Neural Networks (CNNs) by reformulating mean-field CRF inference as a Recurrent Neural Network. This fusion enables end-to-end training via backpropagation for semantic image segmentation tasks, eliminating the need for separate, offline post-processing steps. Our work allows computers to recognize objects in images, what is distinctive about our work is that we also... -
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iOS Tech Frontier
Tanslates high-quality iOS technology, open source libraries
...Instead of simple how-to recipes, the project collects detailed explanations, system internals analyses, and real-world insights into core subsystems like memory management (ARC), threading and Grand Central Dispatch, Objective-C/Swift runtime behavior, UIKit rendering pipelines, and effective use of concurrency. It also covers architectural and performance topics such as dynamic layout optimization, view lifecycle subtleties, Swift language pitfalls, and integration with low-level APIs such as Metal or CoreAnimation. By aggregating authoritative references, experiments, and code snippets, the guide helps developers reason through tradeoffs, debug subtle issues, and architect large-scale iOS systems. -
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ViennaCL
Linear algebra and solver library using CUDA, OpenCL, and OpenMP
ViennaCL provides high level C++ interfaces for linear algebra routines on CPUs and GPUs using CUDA, OpenCL, and OpenMP. The focus is on generic implementations of iterative solvers often used for large linear systems and simple integration into existing projects. -
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Neural Libs
Neural network library for developers
...The project also includes examples of the use of neural networks as function approximation and time series prediction. Includes a special program makes it easy to test neural network based on training data and the optimization of the network. -
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AlgART Java Libraries
Open source library for processing arrays and matrices
...Main features: 63-bit addressing of array elements (64-bit long int indexes), memory model concept (allowing storing data in different schemes from RAM to mapped disk files), wide usage of lazy evaluations, built-in multithreading optimization for multi-core processors, wide set of image processing algorithms over matrices, etc. - please see at the site. Almost all classes and methods are thoroughly documented via JavaDoc (you may read full JavaDoc at the site). -
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libfgen
Library for optimization using a genetic algorithm or particle swarms
libfgen is a library that implements an efficient and customizable genetic algorithm (GA). It also provides particle swarm optimization (PSO) functionality and an interface for real-valued function minimization or model fitting. It is written in C, but can also be compiled with a C++ compiler. Both Linux and Windows are supported. -
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Single header, templatized epxression tree. Assign functors to branches and values to leaves, then call evaluate(). Requires C++11's function<T>. Optional multi-threaded evaluation is dependent on header <future>.
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LSGTL means LLX’s Static Graph Template Library which is a light-weighted header-only template library developed mainly for static graph analysis. LSGTL is expected to be used in laboratories for research purposes mostly.
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Program to performing the complete cycle of neural networks analysis: preparing data, choosing neural network (CasCor, MP, LogRegression, PNN), learning of network, monitoring learning state, ROC-analysis, optimization of network parameters using GA.
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This STL-like C++ library contains classes for long integer numbers processing with using of assembler functions as a backend. Lazy evaluation is also used for optimization. It also contains generic implementation of classical number-theory algorithms.
