《李宏毅深度学习教程》（李宏毅老师推荐👍，苹果书🍎），PDF下载地址：https://github.com/datawhalechina/leedl-tutorial/releases

ROSS is a library written in Python for rotordynamic analysis.

AMrotor - A MATLAB Toolbox for the Simulation of Rotating Machinery

AMrotor - A MATLAB Toolbox for the Simulation of Rotating Machinery

The Second Order Synchroextracting Transform

A comparison of serveral localized time-frequency transforms

1D synchrosqueezed wave packet transforms (SSWPT): Analyzing instantaneous properties (instantaneous frequencies, phases and amplitudes) of non-linear oscillatory signals in a superposition. 2D syn…

Horizontal Synchrosqueezing Transform: Algorithm and Applications

A notebook tutorial series for performing predictive maintenance using machine learning

The ASTRES Toolbox for Mode Extraction of Non-Stationary Multicomponent Signals

TVReg, Version 1.1: Software for 3D Total Variation Regularization

MATLAB package of iterative regularization methods and large-scale test problems. This software is described in the paper "IR Tools: A MATLAB Package of Iterative Regularization Methods and Large-S…

The AIR Tools II toolbox for MATLAB accompanies the publication "AIR Tools II: algebraic iterative reconstruction methods, improved implementation", Hansen, P. C. & Jørgensen, J. S. Numer Algor (20…

邱锡鹏《神经网络与深度学习：案例与实践》PyTorch 实现

Code for reproducing experiments in "Improved Training of Wasserstein GANs"

A PyTorch implementation for Asymmetric Tri-training for Unsupervised Domain Adaptation

A PyTorch implementation for Adversarial Representation Learning for Domain Adaptation

Barnes-Hut t-SNE

Unsupervised Domain Adaptation Papers and Code

A collection of implementations of adversarial domain adaptation algorithms

🧠 A PyTorch implementation of 'Deep CORAL: Correlation Alignment for Deep Domain Adaptation.', ECCV 2016

Joint distribution optimal transportation for domain adaptation

Code to accompany ICML 2018 paper

Correlation Alignment for Domain Adaptation

Stacked Generative Adversarial Networks

Learning Deep Representations of Fine-grained Visual Descriptions