Showing 801-820 of 1,001 projects
A tutorial that takes developers from no deep learning knowledge to implementing GPT using PyTorch.
Instant-NGP in PyTorch+CUDA with PyTorch Lightning for high-quality, high-speed 3D reconstruction and novel view synthesis.
A PyTorch Lightning code guideline and template for building deep learning projects.
A simple PyTorch implementation of Generative Adversarial Networks for generating anime-style faces.
A PyTorch implementation of the Capsule Graph Neural Network (CapsGNN) for graph classification tasks.
A repository for few-shot learning machine learning projects, focused on meta-learning and PyTorch.
An awesome PyTorch library for building NeRF (Neural Radiance Fields) models.
Replicable multi-agent reinforcement learning library with support for PyTorch, Ray, and RLlib.
A PyTorch library for monocular depth estimation using deep learning techniques.
PyTorch implementation of Deep Reinforcement Learning algorithms like TRPO, PPO, A2C, and GAIL.
An open-source toolkit for general OCR research and applications, with integrated training, evaluation, and production-ready OCR systems.
Early stopping implementation for PyTorch, helping prevent overfitting in machine learning models.
Generic Neural Elastic Search, a cloud-native semantic search system based on deep neural network.
A PyTorch implementation of the DeepLab v3+ semantic segmentation model, allowing developers to train their own models.
A PyTorch implementation of the Proximal Policy Optimization (PPO) algorithm for training an AI agent to play Super Mario Bros.
A PyTorch-based deep reinforcement learning library for developing AI agents and algorithms.
A PyTorch implementation of the Quasi-Recurrent Neural Network, up to 16x faster than cuDNN LSTM.
A multi-backend Keras API implementation with support for TensorFlow, JAX, and PyTorch.
A fast communication-overlapping library for tensor/expert parallelism on GPUs, useful for AI/ML applications.
A PyTorch template with implementations of classical CNNs, data augmentation, losses, and common ML algorithms.
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