Search Results for "self learning ai"
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Showing 24 open source projects for "self learning ai"
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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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Go From AI Idea to AI App FastAccess Gemini 3 and 200+ models. Build chatbots, agents, or custom models with built-in monitoring and scaling.
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NFH Self-Improvement Loop
Minimal adversarial framework for AI agent self-modification
NFH Self-Improvement Loop is a conceptual framework and implementation designed to model continuous self-improvement cycles using AI systems. It focuses on creating feedback loops where outputs are evaluated, refined, and reintroduced into the system for further improvement. The project emphasizes iterative learning, allowing systems to evolve over time through repeated evaluation and adjustment. -
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FATE
An industrial grade federated learning framework
FATE (Federated AI Technology Enabler) is the world's first industrial grade federated learning open source framework to enable enterprises and institutions to collaborate on data while protecting data security and privacy. It implements secure computation protocols based on homomorphic encryption and multi-party computation (MPC). Supporting various federated learning scenarios, FATE now provides a host of federated learning algorithms, including logistic regression, tree-based algorithms, deep learning and transfer learning. ... -
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Superduper
Superduper: Integrate AI models and machine learning workflows
Superduper is a Python-based framework for building end-2-end AI-data workflows and applications on your own data, integrating with major databases. It supports the latest technologies and techniques, including LLMs, vector-search, RAG, and multimodality as well as classical AI and ML paradigms. Developers may leverage Superduper by building compositional and declarative objects that out-source the details of deployment, orchestration versioning, and more to the Superduper engine. This... -
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Flower
Flower: A Friendly Federated Learning Framework
A unified approach to federated learning, analytics, and evaluation. Federate any workload, any ML framework, and any programming language. Federated learning systems vary wildly from one use case to another. Flower allows for a wide range of different configurations depending on the needs of each individual use case. Flower originated from a research project at the University of Oxford, so it was built with AI research in mind. -
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OWL
Optimized Workforce Learning for General Multi-Agent Assistance
OWL (Optimized Workforce Learning) is a sophisticated open-source framework built on the CAMEL-AI ecosystem for orchestrating teams of AI agents to collaboratively solve complex, real-world tasks with dynamic planning and automation capabilities. Unlike single-agent systems, it treats task completion as a collaborative workforce where agents take on specialized roles (planning, execution, analysis) and coordinate via a modular multi-agent architecture that supports flexible teamwork across domains. ... -
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EasyZSwoole
swoole, easyswoole, swoole framework
...Let developers write multi-process, step-by-step, and high-available application services with the lowest learning cost and effort. -
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Go 101
An up-to-date (unofficial) knowledge base for Go programming
Go 101 is a series of books on Go programming. Currently, the following books are available. Go (Fundamentals) 101, which focuses on Go syntax/semantics (except custom generics related) and all kinds of runtime related things. Go Generics 101, which explains Go custom generics in detail. Go Optimizations 101, which provides some code performance optimization tricks, tips, and suggestions. Go Details & Tips 101, which collects many details and provides several tips in Go programming. These... -
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NNCF
Neural Network Compression Framework for enhanced OpenVINO
NNCF (Neural Network Compression Framework) is an optimization toolkit for deep learning models, designed to apply quantization, pruning, and other techniques to improve inference efficiency. -
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Tiny CUDA Neural Networks
Lightning fast C++/CUDA neural network framework
This is a small, self-contained framework for training and querying neural networks. Most notably, it contains a lightning-fast "fully fused" multi-layer perceptron (technical paper), a versatile multiresolution hash encoding (technical paper), as well as support for various other input encodings, losses, and optimizers. We provide a sample application where an image function (x,y) -> (R,G,B) is learned. The fully fused MLP component of this framework requires a very large amount of shared... -
Go from Code to Production URL in SecondsSkip the Kubernetes configs. Cloud Run handles HTTPS, scaling, and infrastructure automatically. Two million requests free per month.
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OneFlow
OneFlow is a deep learning framework designed to be user-friendly
...It adheres to the core concept and architecture of static compilation and streaming parallelism and solves the memory wall challenge at the cluster level. world-leading level. Provides a variety of services from primary AI talent training to enterprise-level machine learning lifecycle integrated management (MLOps), including AI training and AI development, and supports three deployment modes of public cloud, private cloud and hybrid cloud. -
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Redwood
The App Framework for Startups
Focus on building your startup, not fighting your framework. Redwood is the full-stack web framework designed to help you grow from side project to startup. Our mission is to help more startups explore more territory, more quickly. We begin by crafting a more integrated framework. We’ve chosen the world’s most popular rendering engine to power Redwood’s web frontend. With React, you’ll have your pick of learning materials, design systems, and trained employees. As your project grows, so will... -
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Rakkas
Bleeding-edge React framework powered by Vite
...If you need a stable React framework try Next.js, Remix, or Gatsby. create-rakkas-app project initializer comes with many features, all of which are optional but we strongly recommend enabling TypeScript and the generation of a demo project on your first try because self-documenting type definitions allow for a smoother learning curve and the demo project source code comes with plenty of comments. -
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BEVFormer
Implementation of BEVFormer, a camera-only framework
3D visual perception tasks, including 3D detection and map segmentation based on multi-camera images, are essential for autonomous driving systems. In this work, we present a new framework termed BEVFormer, which learns unified BEV representations with spatiotemporal transformers to support multiple autonomous driving perception tasks. In a nutshell, BEVFormer exploits both spatial and temporal information by interacting with spatial and temporal space through predefined grid-shaped BEV... -
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golangweekly
Weekly magazine for Go language lovers, published every Sunday
As Go has received more and more attention, there have been more and more related articles, software, and resources. Go Language Lovers Weekly aims to share with you a week of Go language-related content that is worth knowing and learning. Published every Sunday. Contributions, recommended or self-recommended articles/software/resources are welcome, please submit an issue. Welcome to pay attention to our public account and get the weekly magazine as soon as possible. Weekly magazine for Go language lovers, published every Sunday. -
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TorchGAN
Research Framework for easy and efficient training of GANs
The torchgan package consists of various generative adversarial networks and utilities that have been found useful in training them. This package provides an easy-to-use API which can be used to train popular GANs as well as develop newer variants. The core idea behind this project is to facilitate easy and rapid generative adversarial model research. TorchGAN is a Pytorch-based framework for designing and developing Generative Adversarial Networks. This framework has been designed to... -
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Caffe
A fast open framework for deep learning
...Caffe is developed by the Berkeley AI Research (BAIR)/The Berkeley Vision and Learning Center (BVLC) and a great community of contributors that continue to make Caffe state-of-the-art in both code and models. It’s been used in numerous projects, from startup prototypes and academic research projects, to large scale industrial applications. -
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JCLTP
A Java Class Library for Text Processing
JCLTP is a class library designed for processing text. JCLTP is free, open source and developed with the Java programming language. JCLTP is distributed under the GNU license. It incorporates several technologies that enable process information while applying AI techniques, in order to build predictive models for text classification. Through a flexible structure of interfaces and classes, the opportunity to extend, adapt and add functionality JCLTP is provided. Thus, analysis of new types... -
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JCLALtext
Text processing module for JCLAL
JCLALtext is a class library designed to extend the framework JCLAL text tasks. JCLALtext is free, open source and developed with the Java programming language. JCLALtext is distributed under the GNU license. The researcher can use the class library by adding it to your project. -
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Spock Example
Spock example specifications along with ready-to-go Gradle builds
The Spock Example project is a reference repository demonstrating how to set up and use the Spock testing framework with Gradle or Maven. It includes sample Specification classes that illustrate key Spock features—given/when/then style, data-driven testing, mocking/stubbing, and interaction verification. The examples show how to integrate Spock into typical Java/Groovy projects, how to run tests inside IDEs like Eclipse or IntelliJ, and how to align build scripts with Spock’s dependencies.... -
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Simple PHP FrameWork
Main goal is simplicity
Design of a framework in php with MVC framework where the main goal is simplicity for the composition of the backend -
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The aim of ALIVE is to develop new approaches to the engineering of flexible, adaptable distributed service-oriented systems based on the adaptation of social coordination and organisation mechanisms.
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Java framework for agent based application development
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Self-learning web framework allowing new applications to be easily added. First applications deal with arithmetic operations (multiplications, divisions, additions or subtractions tables).
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Self Organization of an adaptive learning framework for pervasive graph structures from grids, utilities to semantic governance. Business, performance and provisioning assurance are converged with EGA, ITIL, eTOM,SOX, HIPPA, CobiT and AC
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