Awesome GitHub RepositoriesSequential Pipelines

Faceai is a computer vision toolkit designed for facial analysis, identity recognition, and image processing. It provides integrated engines for detecting human faces in static images and live video streams, matching facial encodings against identity databases, and mapping facial landmarks to understand geometric structure and alignment. The project enables real-time augmented reality applications, such as applying virtual makeup and digital accessories by scaling assets to detected facial coordinates. It also includes a suite for digital image restoration capable of removing noise, erasing w

This is a collection of machine learning projects, data visualization portfolios, and predictive analytics tools. The repository provides implementation examples for training predictive models, executing data analysis pipelines, and estimating metadata values through historical statistical tables. The project emphasizes evolutionary computing, utilizing genetic algorithms and programming to solve optimization problems. This includes calculating the shortest distance between geographic coordinates and automating the selection of models and hyperparameters within machine learning pipelines. Ad

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Ce projet propose des méthodologies et des guides pour le prompt engineering structuré, les workflows génératifs et les stratégies spécialisées de génération d'images. Il sert de framework pour optimiser les entrées destinées aux grands modèles de langage (LLM) pour des tâches de codage, d'écriture et d'analyse, ainsi que de bibliothèque de techniques pour contrôler les modèles de diffusion. Le projet se distingue par un framework de conception logicielle piloté par l'IA qui convertit les besoins métier en architectures techniques et en code via du prompting orienté domaine. Il implémente également des modèles de workflow d'IA générative utilisant des pipelines de prompts séquentiels et des frameworks cognitifs pour garantir des sorties de modèle prévisibles. La surface de capacités couvre l'architecture logicielle via la modélisation d'API orientée domaine et la génération de langages dédiés (DSL). Elle s'étend à la génération d'images, incluant la liaison structurelle d'images, l'entraînement de modèles personnalisés et le raffinement itératif par inpainting pour corriger les artefacts visuels. Le projet est implémenté sous forme d'une série de Jupyter Notebooks.

Ce projet est une référence technique numérique et une source de manuel axée sur la programmation graphique. Il consiste en une collection de guides de programmation graphique de bas niveau fournis sous forme de fichiers source Markdown conçus pour la distribution numérique et l'étude. Le projet utilise un pipeline de construction de livre électronique Markdown pour transformer le texte source en formats HTML5, EPUB3 et Mobi. Ce processus exploite Pandoc pour convertir des documents techniques structurés en formats de lecture numérique standardisés via un pipeline de traitement séquentiel. Le système couvre le formatage de documents techniques et la distribution de manuels numériques. Il transforme l'écriture technique brute en mises en page de livres numériques soignées pour l'éducation et la référence professionnelles.

Aigcpanel is a visual workflow automation tool and model lifecycle manager designed for generative AI media pipelines. It provides a unified interface to install, launch, and configure both local and remote AI model endpoints, acting as an orchestration platform for large language models and AI tools. The system features a drag-and-drop node editor for chaining AI models and scripts into automated processing pipelines. It distinguishes itself with a breakpoint-aware execution model that allows users to pause and resume long media tasks from specific points in the workflow. Additionally, it in

Narrator is an artificial intelligence system that converts real-time video feeds into natural language audio descriptions. It functions as a multimodal vision narrator and scene descriptor, using computer vision to transform environmental data from a camera into synthetic speech. The tool operates as a pipeline that captures periodic images from a feed and uses a multimodal large language model to analyze visual events. These analyses are then converted via text-to-speech synthesis into a voiceover that describes real-world activities and surroundings. The system supports automated environm

This is a structured deep learning curriculum for programmers, delivered as a collection of Jupyter notebooks. It teaches the fundamentals of training neural networks for computer vision, natural language processing, tabular data analysis, and collaborative filtering using PyTorch and the fastai library. The course is designed to be hands-on, guiding learners from building a training loop from scratch to fine-tuning pretrained models for a variety of practical tasks. The curriculum distinguishes itself by covering the full lifecycle of a deep learning project, from data preparation and augmen