Fan1225's starred repositories
CVAE-GAN_tensorlayer
A CVAE-GAN implementation with tensorlayer1.x
Rotating-machine-fault-data-set
Open rotating mechanical fault datasets (开源旋转机械故障数据集整理)
piecewise-linear-rul
Code repository for the paper "Piecewise-linear modelling with automated feature selection for Li-ion battery end-of-life prognosis"
PyTorch-GAN
PyTorch implementations of Generative Adversarial Networks.
leetcode-master
《代码随想录》LeetCode 刷题攻略：200道经典题目刷题顺序，共60w字的详细图解，视频难点剖析，50余张思维导图，支持C++，Java，Python，Go，JavaScript等多语言版本，从此算法学习不再迷茫！🔥🔥 来看看，你会发现相见恨晚！🚀
AAE-PyTorch
Adversarial autoencoder (basic/semi-supervised/supervised)
pytorch-generative-model-collections
Collection of generative models in Pytorch version.
the-gan-zoo
A list of all named GANs!
generative-models
Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.
neural_artistic_style
Neural Artistic Style in Python
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.
free-programming-books
:books: Freely available programming books
PaddleSpatial
PaddleSpatial is an open-source spatial-temporal computing tool based on PaddlePaddle.
PINNpapers
Must-read Papers on Physics-Informed Neural Networks.
characterizing-pinns-failure-modes
Characterizing possible failure modes in physics-informed neural networks.
pinn_wind_bearing
Python scripts for wind turbine main bearing fatigue life estimation with physics-informed neural networks
prog_algs
The Prognostic Algorithm Package is a python framework for model-based prognostics (computation of remaining useful life) of engineering systems, and provides a set of algorithms for state estimation and prediction, including uncertainty propagation. The algorithms take as inputs prognostic models (from NASA's Prognostics Model Package), and perform estimation and prediction functions. The library allows the rapid development of prognostics solutions for given models of components and systems. Different algorithms can be easily swapped to do comparative studies and evaluations of different algorithms to select the best for the application at hand.
Lhy_Machine_Learning
李宏毅2021/2022/2023春季机器学习课程课件及作业
MachineLearning-LHY
李宏毅2020机器学习课后作业