Search Results for "optimization" - Page 3
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Showing 84 open source projects for "optimization"
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Angular Performance Checklist
Cheatsheet for developing lightning fast progressive Angular apps
...It outlines actionable recommendations — from bundling and minification, tree-shaking, lazy loading, ahead-of-time (AoT) compilation, resource prefetching, caching and compression, to runtime strategies like change-detection optimization (OnPush, detaching change detectors), minimizing DOM operations, optimizing templates, using pure pipes, minimizing watchers, and more. The checklist is designed to be framework-agnostic in many parts, but especially valuable for Angular developers wanting to build responsive, fast-loading, efficient single-page applications. -
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Julia.jl
Curated decibans of Julia programming language
Julia.jl is a curated collection of knowledge resources for the Julia programming language, designed to support high-performance numerical analysis and computational science. The repository aggregates diverse content across domains such as mathematics, physics, data science, optimization, machine learning, and supercomputing. It functions as a structured index, helping developers, researchers, and learners easily find materials to deepen their understanding of Julia’s ecosystem. The project emphasizes community contributions, encouraging users to expand and refine the resource pool. With a wide range of topic-focused documents, it provides both academic and practical references for applied research and development. ... -
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Game-Programmer-Study-Notes
A collection of reading notes from my career as a game programmer
The Game-Programmer-Study-Notes project is a comprehensive collection of study materials aimed at aspiring and professional game developers. It compiles notes, tutorials, and references covering a wide range of topics such as graphics programming, game engine architecture, mathematics, and optimization techniques. The repository is structured to guide learners through fundamental and advanced concepts, making it suitable for both beginners and experienced programmers. It includes insights from industry practices as well as academic knowledge, bridging the gap between theory and real-world application. The content is often supplemented with diagrams, examples, and explanations that clarify complex topics. ... -
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TRFL
TensorFlow Reinforcement Learning
...Pronounced “truffle,” it simplifies the implementation of RL agents by offering reusable components such as loss functions, value estimation tools, and temporal difference (TD) learning operators. The library is designed to integrate seamlessly with TensorFlow, allowing users to define differentiable RL objectives and train models using standard optimization routines. TRFL supports both CPU and GPU TensorFlow environments, though TensorFlow itself must be installed separately. It exposes clean, modular APIs for various RL methods including Q-learning, policy gradient, and actor-critic algorithms, among others. Each function returns not only the computed loss tensor but also a detailed structure containing auxiliary information like TD errors and targets. -
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Differentiable Neural Computer
A TensorFlow implementation of the Differentiable Neural Computer
The Differentiable Neural Computer (DNC), developed by Google DeepMind, is a neural network architecture augmented with dynamic external memory, enabling it to learn algorithms and solve complex reasoning tasks. Published in Nature in 2016 under the paper “Hybrid computing using a neural network with dynamic external memory,” the DNC combines the pattern recognition power of neural networks with a memory module that can be written to and read from in a differentiable way. This allows the... -
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libRSF
A robust sensor fusion library for online localization
...The general idea of factor graphs is to describe the state estimation problem as a graph of nodes (the state variables) that are connected by factors (measurements). The resulting graph optimization problem can be solved by applying non-linear least squares optimization. -
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babel-plugin-macros
Allows you to build simple compile-time libraries
Currently, each babel plugin in the babel ecosystem requires that you configure it individually. This is fine for things like language features but can be a frustrating overhead for libraries that allow for compile-time code transformation as an optimization. babel-plugin-macros defines a standard interface for libraries that want to use compile-time code transformation without requiring the user to add a babel plugin to their build system (other than babel-plugin-macros, which is ideally already in place). There is cpu usage/time overhead; the client needs to run the code to generate these classes every time the page loads. ... -
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TNN
Uniform deep learning inference framework for mobile
...It also has many outstanding advantages such as cross-platform, high performance, model compression, and code tailoring. The TNN framework further strengthens the support and performance optimization of mobile devices on the basis of the original Rapidnet and ncnn frameworks. At the same time, it refers to the high performance and good scalability characteristics of the industry's mainstream open source frameworks, and expands the support for X86 and NV GPUs. On the mobile phone, TNN has been used by many applications such as mobile QQ, weishi, and Pitu. ... -
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NLP Architect
A model library for exploring state-of-the-art deep learning
NLP Architect is an open-source Python library for exploring state-of-the-art deep learning topologies and techniques for optimizing Natural Language Processing and Natural Language Understanding neural networks. The library includes our past and ongoing NLP research and development efforts as part of Intel AI Lab. NLP Architect is designed to be flexible for adding new models, neural network components, data handling methods, and for easy training and running models. NLP Architect is a... -
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Higher
higher is a pytorch library
higher is a specialized library designed to extend PyTorch’s capabilities by enabling higher-order differentiation and meta-learning through differentiable optimization loops. It allows developers and researchers to compute gradients through entire optimization processes, which is essential for tasks like meta-learning, hyperparameter optimization, and model adaptation. The library introduces utilities that convert standard torch.nn.Module instances into “stateless” functional forms, so parameter updates can be treated as differentiable operations. ... -
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DeepLearning
Deep Learning (Flower Book) mathematical derivation
...It is edited by three world-renowned experts, Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Includes linear algebra, probability theory, information theory, numerical optimization, and related content in machine learning. At the same time, it also introduces deep learning techniques used by practitioners in the industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling and practical methods, and investigates topics such as natural language processing, Applications in speech recognition, computer vision, online recommender systems, bioinformatics, and video games. ... -
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Zopfli
Zopfli Compression Algorithm is a compression library
...A companion utility, zopflipng, targets PNGs by trying alternate filter strategies and recompressing IDAT chunks, often achieving additional savings without changing image quality. The codebase includes both a reusable library and ready-to-use CLI tools for bulk optimization in build pipelines. It is frequently used offline—e.g., as a final step in release builds—because decode speed remains normal while files get smaller. -
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Browser diet
The definitive front-end performance guide
...The project was built as a static site powered by DocPad, with content written in Markdown and translated into multiple languages, making it accessible to a global audience. Its tone is intentionally playful (with a “diet” metaphor for trimming page weight) to make performance optimization less intimidating and more approachable. The repository provides the full source of the site, including styles, content, and build pipeline so others can run it locally or fork it to create customized guides. Even though it is not a tool or library, browser-diet has been influential as an educational resource that demystifies front-end performance. -
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TF Quant Finance
High-performance TensorFlow library for quantitative finance
TF Quant Finance is a high-performance library of quantitative finance components built on TensorFlow, aimed at research and production workloads. It implements pricing engines, risk measures, stochastic models, optimizers, and random number generators that are differentiable and vectorized for accelerators. Users can value options and fixed-income instruments, simulate paths, fit curves, and calibrate models while leveraging TensorFlow’s jit compilation and automatic differentiation. The... -
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pytorch-examples
Simple examples to introduce PyTorch
The pytorch-examples project is a collection of concise and practical examples demonstrating how to use PyTorch for machine learning and deep learning tasks. It focuses on clarity and minimalism, providing small, self-contained scripts that illustrate key concepts such as neural network training, optimization, and data handling. The examples cover a range of topics including supervised learning, generative models, and reinforcement learning, making it a valuable resource for both beginners and experienced practitioners. By emphasizing readable code, the repository helps users understand how PyTorch’s imperative programming style enables flexible model development. ... -
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Enlive
Selector-based templating and transformation system for Clojure
...It allows selecting, transforming, and generating HTML fragments using CSS selectors, and supports server-side template composition, dynamic pages, and content rewriting. By default selector-transformation pairs are run sequentially. When you know that several transformations are independent, you can now specify (as an optimization) to process them in lockstep. Note that this doesn't work with fragments selectors. Transformations are now slightly restricted in their return values: a node or a collection of nodes (instead of freely nested collections of nodes). -
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DaNNet
Deep Artificial Neural Network framework using Armadillo
DaNNet is a C++ deep neural network library using the Armadillo library as a base. It is intended to be a small and easy to use framework with no other dependencies than Armadillo. It uses independent layer-wise optimization giving you full flexibility to train your network. -
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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....
