Search Results for "cv"
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Showing 126 open source projects for "cv"
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Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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CV-CUDA
CV-CUDA™ is an open-source, GPU accelerated library
CV-CUDA is an open-source project that enables building efficient cloud-scale Artificial Intelligence (AI) imaging and computer vision (CV) applications. It uses graphics processing unit (GPU) acceleration to help developers build highly efficient pre- and post-processing pipelines. CV-CUDA originated as a collaborative effort between NVIDIA and ByteDance. -
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Academic CV Theme
Easily create a beautiful academic résumé or educational website
theme-academic-cv is a Hugo static site theme specialized for building academic curriculum vitae (CV) websites, personal academic homepages, and scholarly portfolios. The theme is tailored to present publication lists, education, projects, awards, and contact information in a clean, professional layout suitable for faculty, researchers, and scientists. -
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Manfred Awesomic CV
Manfred Awesomic CV
The MAC (or Manfred Awesomic CV) is a(nother) naive attempt to create a standard open-source format to define and share CVs. The format is defined as a JSON Schema to validate CVs stored as JSON files. There are A LOT of CV formats, and some of them are a nice try to define a universal CV format, but we think that a CV should include not only what candidates are or know but also what they are interested in and what they want to become. -
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RenderCV
LaTeX CV generator from a YAML/JSON input file
RenderCV is a LaTeX CV/resume framework. It allows you to create a high-quality CV as a PDF from a YAML file with full Markdown syntax support and complete control over the LaTeX code. RenderCV offers built-in LaTeX and Markdown templates ready to produce high-quality CVs. However, the templates are entirely arbitrary and can easily be updated to leverage RenderCV's capabilities with your custom CV themes. -
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latexcv
A collection of cv and resume templates written in LaTeX
A collection of user-friendly LaTeX CV and résumé templates (packaged within the R Markdown vitae ecosystem), offering simple themes and templates for creating professional CVs without heavy TeX coding. Supports multiple display themes such as classic, modern, sidebar layouts. -
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Arize Phoenix
Uncover insights, surface problems, monitor, and fine tune your LLM
Phoenix provides ML insights at lightning speed with zero-config observability for model drift, performance, and data quality. Phoenix is an Open Source ML Observability library designed for the Notebook. The toolset is designed to ingest model inference data for LLMs, CV, NLP and tabular datasets. It allows Data Scientists to quickly visualize their model data, monitor performance, track down issues & insights, and easily export to improve. Deep Learning Models (CV, LLM, and Generative) are an amazing technology that will power many of future ML use cases. A large set of these technologies are being deployed into businesses (the real world) in what we consider a production setting. -
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AI Job Search
AI-powered job application framework built on Claude Code
AI Job Search is a Claude Code-based framework for managing job applications from profile setup to interview preparation. It helps users turn their CV, documents, and career background into a structured application profile. The workflow can search job portals, score roles by fit, rank openings, and generate tailored application materials. It also creates LaTeX-based CVs and cover letters, then runs a reviewer-style pass before presenting final outputs. Extra commands support interview prep, outcome tracking, skill-gap analysis, profile expansion, and custom job-board skills. ... -
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emgucv
Cross platform .Net wrapper to the OpenCV image processing library
Emgu CV is a cross platform .Net wrapper to the OpenCV image processing library. Allowing OpenCV functions to be called from .NET compatible languages. The wrapper can be compiled by Visual Studio and Unity, it can run on Windows, Linux, Mac OS, iOS and Android. -
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LearnOpenCV
C++ and Python Examples
LearnOpenCV is a large educational repository that provides practical computer vision and deep learning examples in both Python and C++. The project accompanies the LearnOpenCV blog and contains hundreds of hands-on tutorials covering topics such as object detection, image processing, pose estimation, and neural networks. It is structured as a learning resource where each directory corresponds to a specific article or technical walkthrough. The repository supports beginners and advanced... -
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DISTRHO Cardinal
Virtual modular synthesizer plugin
A fully free and self-contained modular synthesizer, based on the popular VCV Rack. Available in AudioUnit/CLAP/LV2/VST2/VST3 plugin formats and as a standalone app for FreeBSD, Linux, macOS, Windows and the Web. -
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GoCV
Go package for computer vision using OpenCV 4 and beyond
...The GoCV package supports the latest releases of Go and OpenCV v4.5.4 on Linux, macOS, and Windows. Our mission is to make the Go language a “first-class” client compatible with the latest developments in the OpenCV ecosystem. Computer Vision (CV) is the ability of computers to process visual information, and perform tasks normally associated with those performed by humans. CV software typically processes video images, then uses the data to extract information in order to do something useful. Since memory allocations for images in GoCV are done through C based code, the go garbage collector will not clean all resources associated with a Mat. ... -
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Data-Juicer
Data processing for and with foundation models
Data-Juicer is an open-source data processing and augmentation framework designed to enhance the quality and diversity of datasets for machine learning tasks. It includes a modular pipeline for scalable data transformation. -
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Magic Portfolio
Build your timeless portfolio with Once UI's Magic Portfolio
...It provides a minimal, beginner-friendly setup that focuses on strong typography, responsive layouts, and restrained motion instead of heavy animations. Content is managed through a structured configuration and MDX-based content system, so you can define sections, projects, blog posts, gallery items, and about/CV information in code-backed content files. The template emphasizes SEO with auto-generated Open Graph/X images, schemas, and metadata derived from the content configuration, making it more discoverable out of the box. It offers flexible feature toggles to enable or disable sections such as blog, work, gallery, and about, as well as options for automatic social link generation and password-protected URLs. -
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DeepSparse
Sparsity-aware deep learning inference runtime for CPUs
A sparsity-aware enterprise inferencing system for AI models on CPUs. Maximize your CPU infrastructure with DeepSparse to run performant computer vision (CV), natural language processing (NLP), and large language models (LLMs). -
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academicpages.github.io
Github Pages template based upon HTML and Markdown for personal
AcademicPages is a ready-made Jekyll theme for academics to build personal websites, blogs, and CV pages. It includes features like publication lists, project showcases, writing blogs, and optimized layouts for easier GitHub Pages deployment. With support for LaTeX rendering, RSS feeds, and responsive design, it's popular among students, researchers, and educators looking to create professional web presences without coding from scratch. -
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A website template for academics
Jekyll website template for personal academic or research group page
This is a statically-generated Jekyll/Liquid/Bootstrap-based website template for academics. I started with the Allan lab webpage and modified it into a personal academic webpage that met my requirements. I worked in a unique set of the features that I desired and could not find in publicly available templates elsewhere. -
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MLJBase.jl
Core functionality for the MLJ machine learning framework
Repository for developers that provides core functionality for the MLJ machine learning framework. MLJ is a Julia framework for combining and tuning machine learning models. This repository provides core functionality for MLJ. -
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ModelScope
Bring the notion of Model-as-a-Service to life
...In particular, with rich layers of API abstraction, the ModelScope library offers unified experience to explore state-of-the-art models spanning across domains such as CV, NLP, Speech, Multi-Modality, and Scientific-computation. Model contributors of different areas can integrate models into the ModelScope ecosystem through the layered APIs, allowing easy and unified access to their models. Once integrated, model inference, fine-tuning, and evaluations can be done with only a few lines of code. -
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AI-Job-Notes
AI algorithm position job search strategy
AI-Job-Notes is a pragmatic notebook for landing roles in machine learning, computer vision, and related engineering tracks. It assembles study paths, checklists, and interview prep materials, but also covers job-search mechanics—portfolio building, resume patterns, and communication tips. The emphasis is on doing: practicing with project ideas, setting up reproducible experiments, and showcasing results that convey impact. It ties technical study (ML/DL fundamentals) to real hiring signals... -
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Super comprehensive deep learning notes
Super Comprehensive Deep Learning Notes
Super comprehensive deep learning notes is a massive and well-structured collection of deep learning notebooks that serve as a comprehensive study resource for anyone wanting to learn or reinforce concepts in computer vision, natural language processing, deep learning architectures, and even large-model agents. The repository contains hundreds of Jupyter notebooks that are richly annotated and organized by topic, progressing from basic Python and PyTorch fundamentals to advanced neural... -
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PyTorch-Tutorial-2nd
CV, NLP, LLM project applications, and advanced engineering deployment
PyTorch-Tutorial-2nd is an open-source educational repository that provides structured tutorials for learning deep learning with the PyTorch framework. The project serves as a practical companion to a second edition of a PyTorch learning guide and is designed to help learners understand neural network concepts through hands-on coding examples. The repository covers a wide range of topics including tensor operations, neural network construction, model training workflows, and optimization... -
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JSCamp InfoJobs
Content and exercises fromJSCamp InfoJobs
...The repo also links to jscamp.dev, where the videos and course materials are hosted, and explains that access is free, with an optional paid certificate that includes extra support like CV review, exercise feedback, and exclusive workshops. Detailed installation requirements are listed (modern browser, VS Code, Live Preview extension, Node.js 20+, Git, Docker, etc. -
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Monaspace
An innovative superfamily of fonts for code
Monaspace is a superfamily of coding typefaces designed to improve the reading rhythm and texture of code while preserving the alignment benefits developers expect. It includes multiple coordinated families and weights, with italics and stylistic alternates that retain code clarity rather than introducing overly decorative forms. The fonts offer thoughtful ligatures and contextual features that handle common programming sequences without distorting spacing or meaning. Variable builds enable... -
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The Grand Complete Data Science Guide
Data Science Guide With Videos And Materials
The Grand Complete Data Science Materials is a repository curated by a data-science educator that aggregates a wide range of learning resources — from basic programming and math foundation to advanced topics in machine learning, deep learning, natural language processing, computer vision, and deployment practices — into a structured, centralized collection aimed at learners seeking a comprehensive path to data science mastery. The repository bundles tutorials, lecture notes, project... -
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Perfect Roadmap To Learn Data Science
Basic To Intermediate Python data science guide
Perfect Roadmap To Learn Data Science In 2025 is an extended, updated learning pathway curated for the modern data-science landscape — blending classical data-analysis, statistics, machine learning, deep learning, computer vision, NLP, as well as current deployment and MLOps practices to prepare learners for data-science careers in 2025. The roadmap is organized to guide learners systematically: starting with Python fundamentals and math/statistics, then progressing through classical...
