Showing 61-80 of 1,001 projects
A private AI platform for building agents, assistants, and enterprise search with multi-model support and API connectivity.
A computer vision library for PyTorch that provides datasets, transforms, and pre-trained models.
PyTorch implementations of Generative Adversarial Networks for AI and machine learning research.
Materials for learning PyTorch, a deep learning framework, from zero to mastery.
An open-source machine learning engineering reference with resources for training, deploying, and scaling AI models.
A comprehensive collection of deep learning tutorials, articles, and resources for developers.
A comprehensive list of PyTorch-related content on GitHub, including models, libraries, and tutorials.
A deep learning model that converts images of mathematical equations into LaTeX code.
Stable Diffusion WebUI Colab notebook for running AI-powered image generation on Google Colab
Restores old photos using generative adversarial networks (GANs) and image manipulation techniques.
CVAT is an industry-leading data engine for machine learning, trusted by teams for annotating data at scale.
A comprehensive speech recognition toolkit with state-of-the-art pretrained models for various speech tasks.
A tiny autograd engine and neural net library with a PyTorch-like API
A tutorial for natural language processing (NLP) using deep learning frameworks like PyTorch and TensorFlow.
A collection of state-of-the-art deep learning scripts for various AI/ML tasks, easily trainable and deployable.
Distributed training framework for deep learning models using popular ML libraries like TensorFlow, Keras, PyTorch, and MXNet.
Burn is a high-performance tensor library and deep learning framework for AI and scientific computing in Rust.
RWKV is an RNN-based language model with high performance, fast training, and flexible transformer-like architecture.
A simple, state-of-the-art NLP framework for tasks like named entity recognition and semantic role labeling.
An open-source AutoML toolkit for automating the machine learning lifecycle, including feature engineering, neural architecture search, and hyperparameter tuning.
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