Showing 421-440 of 1,001 projects
A comprehensive collection of interview questions and answers for AI, computer vision, and LLM topics.
A repository with papers, code, and experiments using deep learning for time series forecasting.
Enables PyTorch, a popular machine learning library, to run on XLA devices like Google TPUs.
A unified framework for large-scale data computation that scales popular Python data tools like NumPy, Pandas, and Scikit-Learn.
A framework for training Stable Baselines3 reinforcement learning agents with hyperparameter optimization.
A PyTorch implementation of the Efficient Neural Architecture Search (ENAS) algorithm for AI model optimization.
A PyTorch repository providing pre-trained models and datasets for common computer vision tasks.
A Python library for removing objects from videos using computer vision and PyTorch.
A PyTorch framework for deep learning on point clouds, with support for various point cloud-related tasks.
BigDL is a distributed deep learning library that allows developers to run TensorFlow, Keras and PyTorch models on Apache Spark/Flink and Ray.
Unofficial PyTorch implementation of Masked Autoencoders Are Scalable Vision Learners
Resource scheduling and cluster management for AI workloads on Kubernetes and other platforms.
An AI-powered tool for training supervised models without manual labeling, using foundation models and multimodal learning.
Optimizes large language models for low-bit precision and sparsity, improving model compression techniques.
Implementation of papers in 100 lines of code
A pretrained PyTorch implementation of the DeepLabV3 and DeepLabV3+ semantic segmentation models for Pascal VOC and Cityscapes datasets.
A PyTorch implementation of the popular UNet neural network architecture for image segmentation tasks.
A PyTorch library for Karras et al. (2022) diffusion models, useful for AI-powered coding tools.
A YOLOv5-based object detection system for detecting safety helmets and restricted areas on construction sites.
A PyTorch optimizer that adapts the learning rate to reduce the variance of gradients, improving training performance.
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