421-WWQ's Stars
microsoft/generative-ai-for-beginners
21 Lessons, Get Started Building with Generative AI 🔗 https://microsoft.github.io/generative-ai-for-beginners/
thuml/Transfer-Learning-Library
Transfer Learning Library for Domain Adaptation, Task Adaptation, and Domain Generalization
znxlwm/pytorch-MNIST-CelebA-GAN-DCGAN
Pytorch implementation of Generative Adversarial Networks (GAN) and Deep Convolutional Generative Adversarial Networks (DCGAN) for MNIST and CelebA datasets
vbezgachev/semi-supervised-gan
PyTorch implementation of semi-supervised GAN
yassouali/awesome-semi-supervised-learning
😎 An up-to-date & curated list of awesome semi-supervised learning papers, methods & resources.
microsoft/Semi-supervised-learning
A Unified Semi-Supervised Learning Codebase (NeurIPS'22)
TorchSSL/TorchSSL
A PyTorch-based library for semi-supervised learning (NeurIPS'21)
CarrotsPie/ZJU-nCov-Hitcarder
kxytim/DLVM_for_process_monitoring
Source code of the paper "Deep Learning of Latent Variable Models for Industrial Process Monitoring".
ieasydevops/AbnormalDetection
Abnormal Detection For Monitor
YixinChen-AI/CVAE-GAN-zoos-PyTorch-Beginner
For beginner, this will be the best start for VAEs, GANs, and CVAE-GAN. This contains AE, DAE, VAE, GAN, CGAN, DCGAN, WGAN, WGAN-GP, VAE-GAN, CVAE-GAN. All use PyTorch.
JGuymont/vae-anomaly-detector
Experiments on unsupervised anomaly detection using variational autoencoder. The variational autoencoder is implemented in Pytorch.
AntixK/PyTorch-VAE
A Collection of Variational Autoencoders (VAE) in PyTorch.
laonahongchen/Bilevel-Optimization-in-Coordination-Game
code implementation for 'Bi-level Actor-Critic for Multi-agent Coordination'(AAAI2020)
nndl/nndl.github.io
《神经网络与深度学习》 邱锡鹏著 Neural Network and Deep Learning
LiDan456/MAD-GANs
Applied generative adversarial networks (GANs) to do anomaly detection for time series data
zhangxu0307/time_series_forecasting_pytorch
time series forecasting using pytorch,including ANN,RNN,LSTM,GRU and TSR-RNN,experimental code
xunzheng/notears
DAGs with NO TEARS: Continuous Optimization for Structure Learning
Diego999/pyGAT
Pytorch implementation of the Graph Attention Network model by Veličković et. al (2017, https://arxiv.org/abs/1710.10903)
google-deepmind/graph_nets
Build Graph Nets in Tensorflow
khundman/telemanom
A framework for using LSTMs to detect anomalies in multivariate time series data. Includes spacecraft anomaly data and experiments from the Mars Science Laboratory and SMAP missions.
WangXuhongCN/OCGNN
my implement of my paper
d2l-ai/d2l-zh
《动手学深度学习》:面向中文读者、能运行、可讨论。中英文版被70多个国家的500多所大学用于教学。
JustusvLiebig/Soft_Sensor_Experiments
BrambleXu/pydata-notebook
利用Python进行数据分析 第二版 (2017) 中文翻译笔记
yzhao062/anomaly-detection-resources
Anomaly detection related books, papers, videos, and toolboxes
petecheng/Time2Graph
Source codes for Time2Graph model.
rob-med/awesome-TS-anomaly-detection
List of tools & datasets for anomaly detection on time-series data.
VachelHU/EvoNet
Time-Series Event Prediction with Evolutionary State Graph, WSDM 2021
iamseancheney/python_for_data_analysis_2nd_chinese_version
《利用Python进行数据分析·第2版》