Search Results for "deep" - Page 17
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Showing 1152 open source projects for "deep"
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Casual Suite
An umbrella package to support a single install point
An umbrella package to support a single installation point for all Casual user interfaces for Emacs. These user interfaces built with Transient are included via the Elisp library header Package-Requires. It is highly recommended that a deep reading of the install procedure for each user interface be done beforehand as each of them has their own recommended customizations to go alongside them. Casual Suite requires Casual Lib which in turn requires a recent installation of Transient 0.6.0+ from either ELPA or MELPA. As an older version of Transient is built-in to the Emacs release, the package manager package.el will not update this package unless the customizable variable package-install-upgrade-built-in is set to t. ... -
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Operator SDK
SDK for building Kubernetes applications. Provides high level APIs
The Operator SDK makes it easier to build Kubernetes native applications, a process that can require deep, application-specific operational knowledge. The Operator SDK provides the tools to build, test, and package Operators. Initially, the SDK facilitates the marriage of an application’s business logic (for example, how to scale, upgrade, or backup) with the Kubernetes API to execute those operations. Over time, the SDK can allow engineers to make applications smarter and have the user experience of cloud services. ... -
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OSRFramework
OSRFramework, the Open Sources Research Framework is a AGPLv3+ project
OSRFramework is a GNU AGPLv3+ set of libraries developed by i3visio to perform Open Source Intelligence collection tasks. They include references to a bunch of different applications related to username checking, DNS lookups, information leaks research, deep web search, regular expressions extraction and many others. At the same time, by means of ad-hoc Maltego transforms, OSRFramework provides a way of making these queries graphically as well as several interfaces to interact with like OSRFConsole or a Web interface. If everything went correctly (we hope so!), it's time for trying usufy., mailfy and so on. ... -
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CTGAN
Conditional GAN for generating synthetic tabular data
CTGAN is a collection of Deep Learning based synthetic data generators for single table data, which are able to learn from real data and generate synthetic data with high fidelity. If you're just getting started with synthetic data, we recommend installing the SDV library which provides user-friendly APIs for accessing CTGAN. The SDV library provides wrappers for preprocessing your data as well as additional usability features like constraints. -
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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Synthetix
Synthetix Solidity smart contracts
...Synthetic assets, or Synths, are assets voted into existence by the community and can come in the form of fiat currencies, cryptocurrencies, stocks, commodities and anything else with a price. Many platforms already leverage the deep liquidity and composability of Synthetix to deliver better trades with lower slippage, hedging, and other unique use cases. -
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RawTherapee
A powerful cross-platform raw photo processing program
RawTherapee is a powerful, cross-platform raw photo processing application targeted at enthusiast and semi-professional photographers who want deep control over image development. It’s written primarily in C++ with a GTK+ front-end, providing a high-precision 32-bit floating-point processing engine along with multiple demosaicing algorithms and advanced color-management features. The program supports a very wide range of camera raw formats (via a patched version of dcraw) as well as non-raw formats such as JPEG, TIFF, and PNG. ... -
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Learn AI Engineering
Learn AI and LLMs from scratch using free resources
Learn AI Engineering is a learning path for AI engineering that consolidates high-quality, free resources across the full stack: math, Python foundations, machine learning, deep learning, LLMs, agents, tooling, and deployment. Rather than a loose bookmark list, it organizes topics into a progression so learners can start from fundamentals and move toward practical, production-oriented skills. It mixes courses, articles, code labs, and videos, emphasizing materials that teach both concepts and hands-on implementation. ... -
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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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OpenCV
Open Source Computer Vision Library
The Open Source Computer Vision Library has >2500 algorithms, extensive documentation and sample code for real-time computer vision. It works on Windows, Linux, Mac OS X, Android, iOS in your browser through JavaScript. Languages: C++, Python, Julia, Javascript Homepage: https://opencv.org Q&A forum: https://forum.opencv.org/ Documentation: https://docs.opencv.org Source code: https://github.com/opencv Please pay special attention to our tutorials!... -
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Vane
Vane is an AI-powered answering engine
...It integrates web search through SearxNG while also supporting discussions, academic sources, image search, and video search to generate citation-backed responses. Vane includes multiple search modes optimized for speed, balanced usage, or deep research depending on the complexity of the query. Its architecture emphasizes modular orchestration, custom provider systems, streaming responses, and widget-based UI enhancements for calculations, weather, and contextual data. Designed as a local-first alternative to commercial AI search engines, the project prioritizes privacy, extensibility, and transparent source-backed answers. -
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Khazix Skills
Digital Life Kazik Open Source AI Skills Collection
...The system emphasizes lifecycle management by embedding versioning, traceability, and metadata directly into generated skill files, allowing future updates and synchronization with the original repository. It also generates wrapper scripts that enable AI agents to interact with the underlying repository functionality without requiring deep manual integration. By enforcing a consistent schema, the project ensures interoperability between skills and simplifies deployment across environments. This makes it especially useful for teams building modular AI agents that rely on external tools or open-source repositories. -
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Custom CSS and JS
Custom CSS Plugin for Visual Studio Code. Based on vscode-icon
Custom CSS and JS is a Visual Studio Code extension that allows users to inject custom CSS and JavaScript into the editor interface, enabling deep visual and behavioral customization beyond what standard themes provide. The tool works by modifying VS Code’s internal styling system, allowing developers to override UI elements such as fonts, colors, spacing, and layout with their own stylesheets. This makes it possible to create highly personalized development environments tailored to individual preferences or workflows. ... -
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Bootstrap Blazor Component
Bootstrap Blazor is an enterprise-level UI component library
Bootstrap Blazor Component is an enterprise-grade UI component library built on top of the Bootstrap framework and Microsoft’s Blazor platform, designed to accelerate the development of modern web applications using C#. It provides a comprehensive set of pre-built, high-quality UI components that follow responsive design principles, enabling developers to create consistent and visually appealing interfaces without starting from scratch. The library supports both Blazor Server and WebAssembly... -
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oh-my-agent
Portable multi-agent harness for .agents-based skills, workflows
...It builds on the idea of modular agent systems, allowing developers to define specialized roles and capabilities that can be combined into larger workflows. The framework emphasizes usability, making it easier to configure agents, assign tasks, and manage interactions without requiring deep expertise in AI system design. It likely includes support for plugins or skills, enabling agents to extend their functionality through integrations with external tools. The system also focuses on coordination, allowing multiple agents to collaborate on complex tasks in a structured manner. Its architecture supports experimentation, making it suitable for both prototyping and iterative development. ... -
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TimeMixer
Decomposable Multiscale Mixing for Time Series Forecasting
TimeMixer is a deep learning framework designed for advanced time series forecasting and analysis using a multiscale neural architecture. The model focuses on decomposing time series data into multiple temporal scales in order to capture both short-term seasonal patterns and long-term trends. Instead of relying on traditional recurrent or transformer-based architectures, TimeMixer is implemented as a fully multilayer perceptron–based model that performs temporal mixing across different resolutions of the data. ... -
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TensorFlow Quantum
Open-source Python framework for hybrid quantum-classical ml learning
...The framework enables researchers and developers to represent quantum circuits as data and integrate them directly into machine learning workflows. By combining classical deep learning techniques with quantum algorithms, the platform allows experimentation with quantum machine learning methods that may offer advantages for certain computational tasks. TensorFlow Quantum integrates with the Cirq quantum computing framework to define and manipulate quantum circuits, while leveraging TensorFlow’s infrastructure for optimization, automatic differentiation, and large-scale computation. ... -
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WeKnora
LLM framework for document understanding and semantic retrieval
WeKnora is an open source framework developed for deep document understanding and semantic information retrieval using large language models. It focuses on analyzing complex and heterogeneous documents by combining multiple processing stages such as multimodal document parsing, vector indexing, and intelligent retrieval. It follows the Retrieval-Augmented Generation (RAG) paradigm, where relevant document segments are retrieved and used by language models to generate accurate, context-aware responses. ... -
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AI Engineer Headquarters
A collection of scientific methods, processes, algorithms
AI-Engineer-Headquarters is a comprehensive educational repository designed to help developers become advanced AI engineers through a structured learning path and practical system-building exercises. The project serves as a curated collection of resources, methodologies, and tools covering topics across the entire artificial intelligence development lifecycle. Rather than focusing only on theoretical knowledge, the repository emphasizes applied learning and encourages engineers to build real... -
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fess
Open source enterprise search server for websites, files, and data
Fess is an open source enterprise search server designed to provide powerful full-text search capabilities across multiple data sources. It enables organizations to quickly deploy a scalable search environment without requiring deep knowledge of underlying search technologies. Fess is built on top of OpenSearch and offers an integrated solution for crawling, indexing, and searching documents from websites, file systems, and various data stores. Fess includes a built-in crawler that can collect content from sources such as databases, CSV files, and shared storage, making it suitable for centralized knowledge discovery. ... -
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Start Machine Learning in 2026
A complete guide to start and improve in machine learning
...The project organizes a large collection of learning resources, including online courses, books, tutorials, research articles, and video lectures that explain fundamental AI concepts. Its structure functions as a learning roadmap that gradually introduces essential topics such as programming, mathematics, statistics, neural networks, and modern deep learning techniques. The repository emphasizes flexibility by allowing learners to choose their own path through the material depending on their interests, preferred learning style, and level of prior knowledge. Many of the resources referenced are free or widely accessible, making the guide practical for self-learners who want to study independently without formal coursework. -
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seq2seq-couplet
Play couplet with seq2seq model
seq2seq-couplet is a deep learning application that generates Chinese couplet responses using a sequence-to-sequence model built with TensorFlow. Its purpose is not general machine translation, but a specialized text generation task in which the model produces a matching second line for a given first line in the style of traditional couplets. The repository includes the code needed to train the model, configure file paths and hyperparameters, and evaluate progress through loss and BLEU score tracking. ... -
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Advanced AI explainability for PyTorch
Advanced AI Explainability for computer vision
pytorch-grad-cam is an open-source library that provides advanced explainable AI techniques for interpreting the predictions of deep learning models used in computer vision. The project implements Grad-CAM and several related visualization methods that highlight the regions of an image that most strongly influence a neural network’s decision. These visualization techniques allow developers and researchers to better understand how convolutional neural networks and transformer-based vision models make predictions. ... -
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MiroFlow
Agent framework that enables tool-use agent tasks
...One of the core innovations of MiroFlow is its use of agent graphs, which enable flexible orchestration of multiple sub-agents and tools in order to complete complex workflows. This architecture allows agents to perform advanced reasoning tasks such as deep research, future event prediction, and multi-step knowledge analysis. The framework emphasizes reliability and scalability by incorporating robust workflow execution, concurrency management, and fault-tolerant design to handle unstable APIs or network conditions. -
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SearChat
Search + Chat = SearChat(AI Chat with Search)
SearChat is an open-source conversational search platform that combines traditional web search engines with large language model reasoning to create an interactive research environment. The project is designed to transform the process of web searching into a multi-turn conversational experience where users can refine queries and explore results through dialogue. Built using a modern monorepo architecture, the system includes a Node.js and Koa backend alongside a Vue 3 and TypeScript frontend... -
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dLLM
dLLM: Simple Diffusion Language Modeling
...The project provides an integrated pipeline that standardizes how diffusion language models are trained, evaluated, and deployed, helping researchers reproduce experiments and compare results more easily. The framework includes scalable training infrastructure inspired by modern deep learning toolkits and supports integrations with widely used libraries for distributed training.
