Projects sharing features with Functional Zoo

Deep recommender models using PyTorch.

A curated list of awesome GitHub guides, articles, sites, tools, projects and resources. 收集这个列表，只是为了更好地使用GitHub,欢迎提交pr和issue。

This project is a community-maintained open source directory that serves as a comprehensive index of React components and libraries. It functions as a technical knowledge base, mapping common development challenges to vetted third-party solutions to help developers accelerate their frontend workflows and avoid reinventing standard interface elements. The directory distinguishes itself through a decentralized, hyperlink-centric architecture that avoids hosting code locally, instead pointing users directly to external repositories. This content is curated through a collaborative model where com

Super simple fit method for PyTorch Modules

:video_game: A curated list of awesome game datasets, and tools to artificial intelligence in games

This project is a comprehensive, curated directory of high-quality libraries, tools, and educational resources for C and C++ development. It serves as an ecosystem discovery index, helping developers navigate the vast landscape of third-party components, frameworks, and technical documentation available for the language. The collection is distinguished by its focus on high-performance systems programming and technical mastery. It provides deep coverage of specialized domains including SIMD-accelerated data processing, compile-time template metaprogramming, and asynchronous event-driven archit

This project is a community-driven directory that aggregates and categorizes high-quality technical resources, tools, and learning materials. It functions as a centralized knowledge management repository, designed to help developers navigate the software development landscape by providing structured access to curated lists and external project references. The directory relies on a collaborative, peer-reviewed workflow where external contributors submit and maintain links through a version-controlled system. This community-maintained approach ensures that the information remains current and re

This project is a PyTorch implementation of the Faster R-CNN architecture for object detection. It provides a framework for identifying multiple object classes and their corresponding bounding boxes within images using a deep learning system. The implementation includes a training pipeline for optimizing models on custom datasets and a utility for converting pretrained weights from external formats into a compatible structure for model initialization. The system covers a two-stage detection pipeline comprising a region proposal network and an ROI pooling layer. It incorporates multi-task los

🔥 Cogitare - A Modern, Fast, and Modular Deep Learning and Machine Learning framework for Python

A curated list of amazingly awesome Git tools, resources and shiny things

Go is a statically typed, compiled programming language designed for building scalable, concurrent software. It provides a memory-safe execution environment that combines a high-performance runtime with a self-hosting compiler toolchain, enabling the creation of statically linked machine code binaries without external dependencies. The language is built around a structural type system that uses interfaces for polymorphism and a concurrency model based on lightweight, stack-based coroutines that communicate through channels. The language distinguishes itself through a runtime that features a c

ICLR 2020 Contrastive Representation Distillation (CRD), and benchmark of recent knowledge distillation methods

This project is a machine learning experiment tracker and event file generator that enables the recording of scalars, images, and histograms to monitor model performance. It functions as an integration bridge that allows training metrics from PyTorch to be logged into files compatible with the TensorBoard dashboard. The system includes a remote log synchronizer designed to stream experiment data to cloud services. This allows for the remote management and analysis of training results and the comparison of datasets across different training runs. The utility covers a broad range of monitoring