Showing 481-500 of 2,040 projects
A powerful PyTorch-based library for time series forecasting with support for AI and machine learning.
XTLS/REALITY is a Go-based library and CLI tool for building secure and performant network applications.
Super-scale images using Residual Dense and Adversarial Networks for computer vision tasks.
This repository provides interview preparation resources for AI and machine learning developers.
A collection of important graph embedding, classification and representation learning papers with implementations for vibe coders.
A small and fast async runtime for Rust developers building concurrent and network-heavy applications.
Native macOS proxy experience built with SwiftUI
A machine learning tutorial introducing readers to a whole machine learning pipeline from scratch.
An engine-agnostic deep learning framework in Java that supports popular AI/ML runtimes like TensorFlow, PyTorch, and ONNX.
A high-performance, coroutine-based PHP framework for building scalable network applications.
A deep learning-based library for person re-identification in PyTorch.
boostorg/beast is a high-performance C++11 library for building HTTP and WebSocket servers and clients.
A PyTorch repository for practicing image classification on the CIFAR-100 dataset using various deep learning models.
A high-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently.
A simple and efficient C++11 network server framework supporting millions of concurrent connections.
A Python library for generating handwritten text using recurrent neural networks (RNNs) and TensorFlow.
An encrypted IPv6 overlay network for scalable decentralized routing, built with Go.
Flux is a machine learning library for the Julia programming language that provides a user-friendly and expressive interface for building neural networks.
AITemplate is a Python framework for rendering neural networks into high-performance CUDA/HIP C++ code, optimized for GPU inference.
A collection of tools for research in neural network interpretability, useful for AI-focused developers.
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