30 repos
Structural designs and mathematical patterns used to define the internal connectivity and data flow of neural networks.
Explore 30 awesome GitHub repositories matching artificial intelligence & ml · Architectures. Refine with filters or upvote what's useful.
Codex is an automated programming tool and generative code assistant designed to interpret developer intent through a natural language interface. It functions as a machine learning model trained on public code repositories to provide intelligent code completion, suggestions, and refactoring within development environme
Employs stacked attention layers to process sequences and capture long-range dependencies within code structures.
This project is a neural text-to-speech engine and voice cloning toolkit designed to generate synthetic speech that mimics the vocal characteristics of a target speaker. It functions as a real-time audio synthesizer, utilizing a deep learning pipeline to convert written text into high-fidelity speech output with minima
Utilizes pre-trained feature extractors to generalize vocal synthesis across diverse and previously unseen speakers.
Llama is a computational framework and runtime environment designed for executing transformer-based neural networks locally. It functions as a generative AI inference engine, enabling the processing of input sequences through pre-trained model weights to produce text completions and structured data outputs directly on
Implements stacked attention layers to process sequences and predict tokens based on learned statistical patterns.
This project is a community-driven library of structured text inputs designed to guide large language models into specific roles, behaviors, and operational modes. It functions as a comprehensive repository of prompt engineering resources, providing reusable templates that allow users to override default model tendenci
Logic-driven prompts interpret user constraints to enforce consistent response patterns and override default model tendencies.
YOLOv5 is a comprehensive computer vision framework designed for end-to-end deep learning, specializing in real-time object detection, image classification, and instance segmentation. It provides a unified toolkit that manages the entire lifecycle of a model, from initial dataset configuration and hyperparameter tuning
Enables modular construction of neural networks by allowing the swapping of detection, segmentation, and classification layers.
Faceswap is a comprehensive framework for automated media manipulation and neural face synthesis. It provides a modular pipeline that manages the entire lifecycle of facial feature extraction, deep learning model training, and image conversion. By coordinating complex computer vision workflows, the system enables users
Synthesizes facial transformations between different identities using generative machine learning models.
This framework provides a development environment for building collaborative systems where autonomous agents interact to solve complex tasks through conversational workflows. It functions as a conversational workflow engine and event-driven runtime, coordinating multi-step processes by translating high-level goals into
Delegates work and shares information to synchronize multiple agents working toward long-horizon objectives.
nanoGPT is a lightweight engine for training and fine-tuning transformer-based language models from scratch. It provides a minimalist codebase designed for educational exploration and rapid experimentation with neural network architectures, utilizing self-attention and feed-forward layers to process sequences and predi
Utilizes stacked self-attention and feed-forward layers to process sequences and capture long-range dependencies.
Ultralytics is a comprehensive computer vision framework designed for training, validating, and deploying deep learning models across a wide range of visual recognition tasks. It provides a unified interface for core operations including object detection, instance segmentation, pose estimation, and image classification
Organizes neural networks into modular backbone, neck, and head components for easier customization.
This project provides a deep learning architecture designed to identify and isolate distinct objects within images by generating precise pixel-level masks. It functions as a browser-based inference engine, enabling the execution of complex machine learning models directly within web environments without requiring serve
Utilizes a specialized deep learning architecture to partition images into distinct segments through precise object isolation.