machine-learning
Implementation of Nougat Neural Optical Understanding for Academic Documents
The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
Graph Neural Network Library for PyTorch
Implementation of the Knowledge Transferable Graph Neural Network (KTGNN), published on WWW2023
基于rasa_nlu，rasa_core，rasa_core_sdk构建的聊天机器人
A feature-rich chat widget for Rasa and Botfront
BARS: Towards Open Benchmarking for Recommender Systems https://openbenchmark.github.io/BARS
A unified, comprehensive and efficient recommendation library
Bringing Old Photo Back to Life (CVPR 2020 oral)
CogDL: A Comprehensive Library for Graph Deep Learning (WWW 2023)
Learn about Machine Learning and Artificial Intelligence
Python数据科学系专栏（pandas、Numpy、SKlearn、Matplotlib）、实战项目（代码、讲解、数据集）
An Industrial Graph Neural Network Framework
Python library for knowledge graph embedding and representation learning.
Paddle Graph Learning (PGL) is an efficient and flexible graph learning framework based on PaddlePaddle
Multi-Hop Logical Reasoning in Knowledge Graphs
中文语音识别; Mandarin Automatic Speech Recognition;
Trained models with fast variant of the "best" LSTM models + legacy models
a machine learning image inpainting task that instinctively removes watermarks from image indistinguishable from the ground truth image
A mindmap summarising Machine Learning concepts, from Data Analysis to Deep Learning.
The open source developer platform to build AI agents and models with confidence. Enhance your AI applications with end-to-end tracking, observability, and evaluations, all in one integrated platform.
PyTorch implementation of AnimeGANv2
🔎 Monitor deep learning model training and hardware usage from your mobile phone 📱
🧑🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), ga…
Neural building blocks for speaker diarization: speech activity detection, speaker change detection, overlapped speech detection, speaker embedding