Search Results for "learning"
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Showing 37 open source projects for "learning"
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Gemini 3 and 200+ AI Models on One PlatformBuild generative AI apps with Vertex AI. Switch between models without switching platforms.
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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MNN
MNN is a blazing fast, lightweight deep learning framework
MNN is a highly efficient and lightweight deep learning framework. It supports inference and training of deep learning models, and has industry leading performance for inference and training on-device. At present, MNN has been integrated in more than 20 apps of Alibaba Inc, such as Taobao, Tmall, Youku, Dingtalk, Xianyu and etc., covering more than 70 usage scenarios such as live broadcast, short video capture, search recommendation, product searching by image, interactive marketing, equity distribution, security risk control. ... -
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ncnn
High-performance neural network inference framework for mobile
...It brings artificial intelligence right at your fingertips with no third-party dependencies, and speeds faster than all other known open source frameworks for mobile phone cpu. ncnn allows developers to easily deploy deep learning algorithm models to the mobile platform and create intelligent APPs. It is cross-platform and supports most commonly used CNN networks, including Classical CNN (VGG AlexNet GoogleNet Inception), Face Detection (MTCNN RetinaFace), Segmentation (FCN PSPNet UNet YOLACT), and more. ncnn is currently being used in a number of Tencent applications, namely: QQ, Qzone, WeChat, and Pitu. -
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MediaPipe
Cross-platform, customizable ML solutions for live and streaming media
...Provides segmentation masks for prominent humans in the scene. MediaPipe Face Mesh is a face geometry solution that estimates 468 3D face landmarks in real-time even on mobile devices. It employs machine learning (ML) to infer the 3D surface geometry, requiring only a single camera input without the need for a dedicated depth sensor. Utilizing lightweight model architectures together with GPU acceleration throughout the pipeline, the solution delivers real-time performance-critical for live experiences. Human pose estimation from video plays a critical role in various applications such as quantifying physical exercises, sign language recognition, and full-body gesture control. ... -
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GSY Flutter Demo
Collection of independent Flutter example components
This repository is a comprehensive showcase of Flutter capabilities through a large set of demos, patterns, and best practices. It includes examples for UI layouts, animations, navigation, networking, state management, and more. The project is often used as a learning playground for developers to see practical implementations of both simple widgets and complex app structures. It demonstrates integrations with APIs, database persistence, and plugin usage, making it a real-world reference app. The codebase is modular, so learners can explore sections independently without needing to understand the entire app at once. ... -
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XNNPACK
High-efficiency floating-point neural network inference operators
XNNPACK is a highly optimized, low-level neural network inference library developed by Google for accelerating deep learning workloads across a variety of hardware architectures, including ARM, x86, WebAssembly, and RISC-V. Rather than serving as a standalone ML framework, XNNPACK provides high-performance computational primitives—such as convolutions, pooling, activation functions, and arithmetic operations—that are integrated into higher-level frameworks like TensorFlow Lite, PyTorch Mobile, ONNX Runtime, TensorFlow.js, and MediaPipe. ... -
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Android Open Project
A categorized collection of Android Open Source Projects
Android Open Project is a highly popular and comprehensive collection of open-source Android projects and libraries curated to help Android developers explore real-world implementations, reusable components, and best practices across the Android ecosystem. Rather than being a single app or library, this repository aggregates multiple independent projects, demos, and code examples covering a wide range of Android development topics — from UI components and network utilities to architectural... -
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SuperAgent
Ajax for Node.js and browsers (JS HTTP client)
SuperAgent is light-weight progressive ajax API crafted for flexibility, readability, and a low learning curve after being frustrated with many of the existing request APIs. It also works with Node.js! Small progressive client-side HTTP request library, and Node.js module with the same API, supporting many high-level HTTP client features. Browser-ready versions of this module are available via jsdelivr, unpkg, and also in the node_modules/superagent/dist folder in downloads of the superagent package. ... -
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SurvivalManual
Libre Survival Manual for Android with offline in mind
...It is fully functional offline, which is important in the case of a catastrophe. But it doesn't have to be used only in emergency situations, it can also be useful for outdoor trips, walks, camps, and learning about nature and yourself truly. This is not only fun, but you can also train skills (fire, build shelter, ...) that you may need in a catastrophe. Some things work best with practice in a relaxed environment, so you also have time for some experiments. The refugees also are welcome to use this application to prepare and guide you for your dangerous journey. ... -
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Amazon Chime SDK for Android
An Android client library for integrating multi-party communications
The Amazon Chime SDK for Android makes it easy to add collaborative audio calling, video calling, and screen share viewing features to Android applications by using the same infrastructure services that power meetings on the Amazon Chime service. This Amazon Chime SDK for Android works by connecting to meeting session resources that you have created in your AWS account. The SDK has everything you need to build custom calling and collaboration experiences in your Android application,... -
AI-generated apps that pass security reviewRetool lets you generate dashboards, admin panels, and workflows directly on your data. Type something like “Build me a revenue dashboard on my Stripe data” and get a working app with security, permissions, and compliance built in from day one. Whether on our cloud or self-hosted, create the internal software your team needs without compromising enterprise standards or control.
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Flutter Vignettes
A collection of fun Flutter experiments, created by gskinner
...It’s not only a code resource but also a design showcase, blending engineering and artistry. By presenting small, focused experiments, flutter_vignettes encourages experimentation and learning while illustrating Flutter’s strengths in rapid UI prototyping. -
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Uno Platform
Build Mobile, Desktop and WebAssembly apps
...Free and paid support is available by both community and our team. Chats, tickets or even screen sharing! Seeing is believing. Learn by doing. See code samples our engineers put out for fun, learning and reuse. Explore the possibilities Uno Platform opens. -
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Flutter Architecture Samples
TodoMVC for Flutter
...Its goal is educational: to help developers compare architecture styles, understand trade-offs, and see best practices in action. You can use the samples in this project as a learning reference, as a roughly apples-to-apples comparison of different approaches, or as a starting point for creating your own apps. The focus of this project is on demonstrating how to structure your code, design your architecture, and the eventual impact of adopting these patterns on testing and maintaining your app. You can use the techniques demonstrated here in many different ways to build apps. ... -
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Just download the above zip file, extract it and then open the index.html file on internet browsers like Firefox ( preferable ) or Google Chrome.
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LakshmiDROP
Fixed Income Analytics, Portfolio Construction, Asset Backed Cost
DROP implements the model libraries and provides systems for fixed income valuation and adjustments, asset allocation and transaction cost analytics, and supporting libraries in numerical optimization and statistical learning. DROP is composed of four main libraries: [Asset Allocation] (https://lakshmidrip.github.io/DROP/AssetAllocationModule.html) [Fixed Income Analytics] (https://lakshmidrip.github.io/DROP/FixedIncomeModule.html) [Numerical Optimization] (https://lakshmidrip.github.io/DROP/NumericalOptimizerModule.html) [Statistical Learning] (https://lakshmidrip.github.io/DROP/StatisticalLearningModule.html) -
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RE/flex lexical analyzer generator
The regex-centric, fast lexical analyzer generator for C++
A C++ high-performance regex library and Flex-compatible lexical analyzer generator with full Unicode support, new indentation anchors, lazy quantifiers, and many other modern features. Accepts Flex lexer specification syntax and is compatible with Bison/Yacc parsers. Generates reusable source code that is easy to understand. Supports fast scanning of UTF-8/16/32 files, strings, and streams. The reflex scanner generator generates clean C++ lexer class code that is thread-safe. Generates... -
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pipeless
A computer vision framework to create and deploy apps in minutes
Pipeless is an open-source computer vision framework to create and deploy applications without the complexity of building and maintaining multimedia pipelines. It ships everything you need to create and deploy efficient computer vision applications that work in real-time in just minutes. Pipeless is inspired by modern serverless technologies. It provides the development experience of serverless frameworks applied to computer vision. You provide some functions that are executed for new... -
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NatML
High performance, cross-platform machine learning for Unity Engine
NatML allows developers to integrate machine learning into their Unity applications in under five lines of code with zero infrastructure. NatML completely removes the need to have any experience with machine learning in order to take advantage of the features it can provide. NatML takes advantage of hardware machine learning accelerators, like CoreML on iOS and macOS, NNAPI on Android, and DirectML on Windows. -
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GitLab CE Server For Local Intranets
The Free & Popular Community git Server in a Complete Virtual Machine
This VM is created for 2 reasons: 1. Very little initial setup work required to make a GIT Server live, within minutes. 2. This system should keep running for Years, without requiring Updates / Breakages. If you are new to Virtual Machines, then please watch the Video below ( taken from my other project. just replace td with gi wherever mentioned ) After starting this VM, please login to its administration panel with: Website Address: https://gi.local/ ( Accept Any Warnings due to... -
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KotlinDL
High-level Deep Learning Framework written in Kotlin
...This project aims to make Deep Learning easier for JVM and Android developers and simplify deploying deep learning models in production environments. -
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Beautiful Responsive Website Templates
Templates For Learning & Implementation - Quickly Create Your Websites
Categories : Website Design, Website Development, Website Creation, Website Templates, Responsive Website, Beautiful Website -
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MACE
Deep learning inference framework optimized for mobile platforms
Mobile AI Compute Engine (or MACE for short) is a deep learning inference framework optimized for mobile heterogeneous computing on Android, iOS, Linux and Windows devices. Runtime is optimized with NEON, OpenCL and Hexagon, and Winograd algorithm is introduced to speed up convolution operations. The initialization is also optimized to be faster. Chip-dependent power options like big.LITTLE scheduling, Adreno GPU hints are included as advanced APIs. -
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TNN
Uniform deep learning inference framework for mobile
TNN, a high-performance, lightweight neural network inference framework open sourced by Tencent Youtu Lab. 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... -
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MMdnn
Tools to help users inter-operate among deep learning frameworks
MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML. MMdnn is a comprehensive and cross-framework tool to convert, visualize and diagnose deep learning (DL) models. The "MM" stands for model management, and "dnn" is the acronym of deep neural network. -
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Processing
Source code for the processing core and development environment
Processing is a flexible software sketchbook and a language for learning how to code within the context of the visual arts. Since 2001, Processing has promoted software literacy within the visual arts and visual literacy within technology. There are tens of thousands of students, artists, designers, researchers, and hobbyists who use processing for learning and prototyping. The core Processing software is augmented by libraries and tools contributed through the community. ... -
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abu
Abu quantitative trading system (stocks, options, futures, bitcoin)
Abu Quantitative Integrated AI Big Data System, K-Line Pattern System, Classic Indicator System, Trend Analysis System, Time Series Dimension System, Statistical Probability System, and Traditional Moving Average System conduct in-depth quantitative analysis of investment varieties, completely crossing the user's complex code quantification stage, more suitable for ordinary people to use, towards the era of vectorization 2.0. The above system combines hundreds of seed quantitative models,...
