williazhang Goto Github PK

ailearning

AiLearning: 机器学习 - MachineLearning - ML、深度学习 - DeepLearning - DL、自然语言处理 NLP

algorithm_interview_notes-chinese

2018/2019/校招/春招/秋招/算法/机器学习(Machine Learning)/深度学习(Deep Learning)/自然语言处理(NLP)/C/C++/Python/面试笔记

compressivesensingdictionarylearning

Compressive Sensing using Sparse Dictionary Learning

cybernetics

控制论相关资料

deep-learning-books

Books for machine learning, deep learning, math, NLP, CV, RL, etc. 一些机器学习、深度学习等相关话题的书籍。

deep-learning-with-python-notebooks

Jupyter notebooks for the code samples of the book "Deep Learning with Python"

deeplearning

深度学习入门教程, 优秀文章, Deep Learning Tutorial

deeplearningbook-chinese

Deep Learning Book Chinese Translation

dfdl

Discriminative Feature-oriented Dictionary Learning

dictionary-learning

Dictionary Learning and Sparse Coding for unsupervised clustering

dictol

DICTOL - A Dictionary Learning Toolbox in Matlab and Python

dvcp-te

Damn Vulnerable Chemical Process - Tennessee Eastman

dynamic-graph-embedding-for-fault-detection

electricity_load_forecast

Electricity Load and Price Forecasting

gcdl---generalized-coupled-dictionary-learning-algorithm

This is the implementation for the paper : Generalized Coupled Dictionary Learning Algorithm TIP

girls-in-ai

免费学代码系列：小白python入门、数据分析data analyst、机器学习machine learning、深度学习deep learning、kaggle实战

kernel-principal-component-analysis-kpca

KPCA for dimensionality reduction, feature extraction , fault detection, and fault diagnosis

lihang

Statistical learning methods, 统计学习方法 [李航] 值得反复读. [笔记, 代码, notebook, 参考文献, Errata]

lrslibrary

Low-Rank and Sparse Tools for Background Modeling and Subtraction in Videos

lstm-load-forecasting

Electricity load forecasting with LSTM (Recurrent Neural Network)

lstm-svm-classify

LSTM和SVM实现设备故障诊断

matlab

matlabfunc

Matlab codes for feature learning

multi-objective-optimization-of-distributed-energy-systems-under-grid-faults

Recently, riding through grid faults and supporting the grid voltage by using grid-connected converters (GCCs) have become major requirements reflected in the grid codes. This paper presents a novel reference current generation scheme with the ability to support the grid voltage by injecting a proper set of positive/negative active/reactive currents by using four controlling parameters. Analytical expressions are proposed to obtain the optimal values of these parameters under any grid voltage condition. The optimal performances can be obtained by achieving the following objectives: first, compliance with the phase voltage limits, second, maximized active and reactive power delivery, third, minimized fault currents, and fourth reduced oscillations on the active and reactive powers. These optimal behaviors bring significant advantages to emerging GCCs, such as increasing the efficiency, lowering the dc-link ripples, improving ac system stability, and avoiding equipment tripping. Simulation and experimental results verify the analytical results and the proposed expressions.

multimodal_dictionary_learning

The code for the paper "Multimodal Task-driven Dictionary Learning for Image Classification".

osdl

Online Sparse Dictionary Learning Algorithm

phd-bibliography

References on Optimal Control, Reinforcement Learning and Motion Planning

power-system-fault-analysis

MATLAB Unbalanced Power System Fault Analysis

prml

Repository of notes, code and notebooks in Python for the book Pattern Recognition and Machine Learning by Christopher Bishop

pyda-2e-zh

:book: [译] 利用 Python 进行数据分析 · 第 2 版