papermann's starred repositories
awosome-cs
计算机专业大学四年学习指南
Best-websites-a-programmer-should-visit-zh
程序员应该访问的最佳网站中文版
LeetCode021
🚀 LeetCode From Zero To One & 题单整理 & 题解分享 & 算法模板 & 刷题路线，持续更新中...
DataStructure
根据王道考研编排
Transfer-Learning-Library
Transfer Learning Library for Domain Adaptation, Task Adaptation, and Domain Generalization
pytorch-tutorial
PyTorch Tutorial for Deep Learning Researchers
Few-shot-Transfer-Learning
Few-shot Transfer Learning for Intelligent Fault Diagnosis of Machine
Maximum-Mean-Discrepancy-Variational-Autoencoder
A PyTorch implementation of the MMD-VAE, an Information-Maximizing Variational Autoencoder (InfoVAE) based off of the TensorFlow implementation published by the author of the original InfoVAE paper.
deep-transfer-learning
A collection of implementations of deep domain adaptation algorithms
Fault_diagnosis_ballbearing_wavelet
Bearing fault diagnosis is important in condition monitoring of any rotating machine. Early fault detection in machinery can save millions of dollars in emergency maintenance cost. Different techniques are used for fault analysis such as short time Fourier transforms (STFT), Wavelet analysis (WA), cepstrum analysis, Model based analysis, etc. we have doing detecting bearing faults using FFT and by using Wavelet analysis more specifically wavelet Analysis up to two levels of approximations and detail components. The analysis is carried out offline in MATLAB. Diagnosing the faults before in hand can save the millions of dollars of industry and can save the time as well. It has been found that Condition monitoring of rolling element bearings has enabled cost saving of over 50% as compared with the old traditional methods. The most common method of monitoring the condition of rolling element bearing is by using vibration signal analysis. Measure the vibrations of machine recorded by velocity
Contrastive-Adaptation-Network-for-Unsupervised-Domain-Adaptation
pytorch implementation for Contrastive Adaptation Network
Dassl.pytorch
A PyTorch toolbox for domain generalization, domain adaptation and semi-supervised learning.
ant-learn-pandas
pandas学习课程代码仓库
HARTransferLearning
Transfer Learning from one dataset to another different dataset using Maximum Mean Discrepancy (MMD)
Dive-into-DL-PyTorch
本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet实现改为PyTorch实现。
Rotating-machine-fault-data-set
Open rotating mechanical fault datasets (开源旋转机械故障数据集整理)
transferlearning
Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习
Hands-on-Machine-Learning
A series of Jupyter notebooks with Chinese comment that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.