chenlei93

chenlei93's Stars

• wh201906/SerialTest

  Data transceiver(monitor)/realtime plotter/shortcut/file transceiver over serial port/Bluetooth/network on Windows/Linux/Android/macOS | 跨平台串口/蓝牙/网络调试助手，带数据收发/实时绘图/快捷发送/文件收发面板，可在PC和Android设备上使用

  Language:C++1.2k226

• iShareStuff/Backup-Plugin-for-Zotero

  Backup Plugin for Zotero

  Language:TypeScript371

• Avik-Jain/100-Days-Of-ML-Code

  100 Days of ML Coding

  45.6k10.6k

• mengchaoheng/SCUT_thesis

  华南理工大学硕博士学位论文模板(LaTeX)。Latex templates for the thesis of South China University of Technology

  Language:TeX30659

• chrischoy/3D-R2N2

  Single/multi view image(s) to voxel reconstruction using a recurrent neural network

  Language:Python1.4k295

• openMVG/awesome_3DReconstruction_list

  A curated list of papers & resources linked to 3D reconstruction from images.

  4.2k832

• zotero-chinese/styles

  中文 CSL 样式

  Language:XML5.2k836

• YyzHarry/imbalanced-semi-self

  [NeurIPS 2020] Semi-Supervision (Unlabeled Data) & Self-Supervision Improve Class-Imbalanced / Long-Tailed Learning

  Language:Python734116

• AntoineAugusti/bagging-boosting-random-forests

  Bagging, boosting and random forests in Matlab

  Language:Matlab2414

• mbhai002/wine-quality-randomForest-vs-KNN-

  comparison knn and random forest + feature selection DFO (Evolutionary computing)

  Language:Matlab2

• karpathy/Random-Forest-Matlab

  A Random Forest implementation for MATLAB. Supports arbitrary weak learners that you can define.

  Language:Matlab183121

• MurphyWan/Python-first-Practice

  Only use 3 days to get the basic concept of Python (Chinese Version | Data Scientist Direction)

  Language:Python494176

• phuijse/bagging_pu

  Simple sklearn based python implementation of Positive-Unlabeled (PU) classification using bagging based ensembles

  Language:Jupyter Notebook9019

• iptv-org/iptv

  Collection of publicly available IPTV channels from all over the world

  Language:JavaScript86.9k2.7k

• iqiukp/SVDD-MATLAB

  MATLAB Code for abnormal detection using Support Vector Data Description (SVDD).

  Language:MATLAB7822

• rom1v/scrcpy

  Display and control your Android device

  Language:C313

• scikit-learn-contrib/imbalanced-learn

  A Python Package to Tackle the Curse of Imbalanced Datasets in Machine Learning

  Language:Python6.9k1.3k

• ufoym/imbalanced-dataset-sampler

  A (PyTorch) imbalanced dataset sampler for oversampling low frequent classes and undersampling high frequent ones.

  Language:Python2.3k264

• PRML/PRMLT

  Matlab code of machine learning algorithms in book PRML

  Language:MATLAB6k2.2k

• ZhaoZhibin/DL-based-Intelligent-Diagnosis-Benchmark

  Source codes for the paper "Deep Learning Algorithms for Rotating Machinery Intelligent Diagnosis: An Open Source Benchmark Study"

  Language:Python632171

• nagdevAmruthnath/Predictive-Maintenance

  A notebook tutorial series for performing predictive maintenance using machine learning

  Language:Jupyter Notebook14474

• trahasch/awesome-public-datasets

  An awesome list of high-quality open datasets in public domains (on-going).

  55

• SajadAHMAD1/Chaotic-GSA-for-Engineering-Design-Problems

  All nature-inspired algorithms involve two processes namely exploration and exploitation. For getting optimal performance, there should be a proper balance between these processes. Further, the majority of the optimization algorithms suffer from local minima entrapment problem and slow convergence speed. To alleviate these problems, researchers are now using chaotic maps. The Chaotic Gravitational Search Algorithm (CGSA) is a physics-based heuristic algorithm inspired by Newton's gravity principle and laws of motion. It uses 10 chaotic maps for global search and fast convergence speed. Basically, in GSA gravitational constant (G) is utilized for adaptive learning of the agents. For increasing the learning speed of the agents, chaotic maps are added to gravitational constant. The practical applicability of CGSA has been accessed through by applying it to nine Mechanical and Civil engineering design problems which include Welded Beam Design (WBD), Compression Spring Design (CSD), Pressure Vessel Design (PVD), Speed Reducer Design (SRD), Gear Train Design (GTD), Three Bar Truss (TBT), Stepped Cantilever Beam design (SCBD), Multiple Disc Clutch Brake Design (MDCBD), and Hydrodynamic Thrust Bearing Design (HTBD). The CGSA has been compared with seven state of the art stochastic algorithms particularly Constriction Coefficient based Particle Swarm Optimization and Gravitational Search Algorithm (CPSOGSA), Standard Gravitational Search Algorithm (GSA), Classical Particle Swarm Optimization (PSO), Biogeography Based Optimization (BBO), Continuous Genetic Algorithm (GA), Differential Evolution (DE), and Ant Colony Optimization (ACO). The experimental results indicate that CGSA shows efficient performance as compared to other seven participating algorithms.

  Language:MATLAB9226

• xinwangliu/Multi-Kernel-Extreme-Learning-Machine

  Language:Matlab1712

• srijanee/DGA

  A neural network model implemented in MATLAB to predict various faults in transformers using Dissolved Gas Analysis.

  Language:Matlab177

• Saleh860/DGA

  Dissolved Gas Analysis

  Language:MATLAB174

• piotrmirowski/DGA

  Code for the 2012 IEEE Transactions on Power Delivery paper on "Statistical Machine Learning and Dissolved Gas Analysis: A Review"

  Language:MATLAB125

• MrLevo520/Mini-Python-Project

  用Python做些有趣的项目

  Language:Jupyter Notebook866220

• ethanwillis/zotero-scihub

  A plugin that will automatically download PDFs of zotero items from sci-hub

  Language:TypeScript3.6k180

• ispamm/Lynx-Toolbox

  Lynx Matlab Toolbox

  Language:Matlab2310