cc386

cc386's Stars

• HuangCongQing/Algorithms_MathModels

  【国赛】【美赛】数学建模相关算法 MATLAB实现（2018年初整理）

  Language:MATLAB2.1k322535

• OverLordGoldDragon/ssqueezepy

  Synchrosqueezing, wavelet transforms, and time-frequency analysis in Python

  Language:Python643196496

• lukasruff/Deep-SVDD

  Repository for the Deep One-Class Classification ICML 2018 paper

  Language:Python247121465

• snowch/movie-recommender-demo

  This project walks through how you can create recommendations using Apache Spark machine learning. There are a number of jupyter notebooks that you can run on IBM Data Science Experience, and there a live demo of a movie recommendation web application you can interact with. The demo also uses IBM Message Hub (kafka) to push application events to topic where they are consumed by a spark streaming job running on IBM BigInsights (hadoop).

  Language:Jupyter Notebook10011357

• rsyamil/cnn-regression

  A simple guide to a vanilla CNN for regression, potentially useful for engineering applications.

  Language:Jupyter Notebook411011

• Ashleshk/Machine-Learning-Stanford-Andrew-Ng

  # Machine Learning (Coursera) This is my solution to all the programming assignments and quizzes of Machine-Learning (Coursera) taught by Andrew Ng. After completing this course you will get a broad idea of Machine learning algorithms. Try to solve all the assignments by yourself first, but if you get stuck somewhere then feel free to browse the code. ## Contents * Lectures Slides * Solution to programming assignment * Solution to Quizzes by Andrew Ng, Stanford University, [Coursera](https://www.coursera.org/learn/machine-learning/home/welcome) ### Week 1 - [X] Videos: Introduction - [X] Quiz: Introduction - [X] Videos: Linear Regression with One Variable - [X] Quiz: Linear Regression with One Variable ### Week 2 - [X] Videos: Linear Regression with Multiple Variables - [X] Quiz: Linear Regression with Multiple Variables - [X] Videos: Octave/Matlab Tutorial - [X] Quiz: Octave/Matlab Tutorial - [X] Programming Assignment: Linear Regression ### Week 3 - [X] Videos: Logistic Regression - [X] Quiz: Logistic Regression - [X] Videos: Regularization - [X] Quiz: Regularization - [X] Programming Assignment: Logistic Regression ### Week 4 - [X] Videos: Neural Networks: Representation - [X] Quiz: Neural Networks: Representation - [X] Programming Assignment: Multi-class Classification and Neural Networks ### Week 5 - [X] Videos: Neural Networks: Learning - [X] Quiz: Neural Networks: Learning - [X] Programming Assignment: Neural Network Learning ### Week 6 - [X] Videos: Advice for Applying Machine Learning - [X] Quiz: Advice for Applying Machine Learning - [X] Videos: Programming Assignment: Regularized Linear Regression and Bias/Variance - [X] Machine Learning System Design - [X] Quiz: Machine Learning System Design ### Week 7 - [X] Videos: Support Vector Machines - [X] Quiz: Support Vector Machines - [X] Programming Assignment: Support Vector Machines ### Week 8 - [X] Videos: Unsupervised Learning - [X] Quiz: Unsupervised Learning - [X] Videos: Dimensionality Reduction - [X] Quiz: Principal Component Analysis - [X] Programming Assignment: K-Means Clustering and PCA ### Week 9 - [X] Videos: Anomaly Detection - [X] Quiz: Anomaly Detection - [X] Videos: Recommender Systems - [X] Quiz: Recommender Systems - [X] Programming Assignment: Anomaly Detection and Recommender Systems ### Week 10 - [X] Videos: Large Scale Machine Learning - [X] Quiz: Large Scale Machine Learning ### Week 11 - [X] Videos: Application Example: Photo OCR - [X] Quiz: Application: Photo OCR ## Certificate * [Verified Certificate]() ## References [[1] Machine Learning - Stanford University](https://www.coursera.org/learn/machine-learning)

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• hustcxl/CXL_Notes

  Study Notes

  Language:Jupyter Notebook22109

• stevenxchung/Stanford-Machine-Learning-Coursework

  Topics - Linear Regression, Logistic Regression, Regularization, Neural Networks, System Design, Support Vector Machines, Unsupervised Learning (k-Means algorithm for clustering), Dimensionality Reduction (principal components analysis), Anomaly Detection, Recommender Systems, Large Scale Machine Learning, and Photo Optical Character Recognition.

  Language:MATLAB210011

• KentaItakura/Crack-detection-using-one-class-SVM

  This demo shows how to detect the crack images using one-class SVM using MATLAB.

  Language:MATLAB18204

• nicococo/ClusterSvdd

  Cluster Support Vector Data Description

  Language:Python18315

• coding-raccoon/Optimization

  This is a repository about the code of those common optimzations.

  Language:Python17102

• sumitsomans/TwinSVM

  MATLAB Implementation of Twin Support Vector Machines

  Language:MATLAB15009

• laurafroelich/tensor_classification

  Code for tensor classification.

  Language:MATLAB13373

• spzhuang/support-tensor-machine

  Try to realize the algorithm "support tensor machine"

  Language:MATLAB13117

• boutalbi/TensorClus

  TensorClus, Tensor co-clustering, text mining, clustering, multiple graphs

  Language:Jupyter Notebook12103

• JasonXu12/SVM-and-Machine-Learning

  This is an one class classification problem, which is based on support vecter domain description （SVDD）and Sequential minimal optimization（SMO）algorithm. This work has been tested on pubilc data set, like Iris data set from http://archive.ics.uci.edu/ml/datasets/Iris.

  Language:MATLAB10208

• pepecalvi93/LSSTM

  Least Squares Support Tensor Machine in Python

  Language:Python10124

• PrithivirajManiram/Robotic-EXoskeleton-for-Arm-Rehabilitation-REXAR-

  Rehabilitation of people afflicted with elbow joint ailments is quite challenging. Studies reveal that rehabilitation through robotic devices exhibits promising results, in particular exoskeleton robots. In this work, 1 degree of freedom active upper-limb exoskeleton robot with artificial intelligence aided myoelectric control system has been developed for elbow joint rehabilitation. The raw surface electromyogram (sEMG) signals from seventeen different subjects for five different elbow joint angles were acquired using the Myo armband. Time-domain statistical features such as waveform length, root mean square, variance, and a number of zero crossings were extracted and the most advantageous feature was investigated for Artificial Neural Network (ANN) – a backpropagation neural network with Levenberg-Marquardt training algorithm and Support Vector Machine (SVM) – with Gaussian kernel. The results show that waveform length consumes the least amount of computation time. With waveform length as an input feature, ANN and SVM exhibited an average overall classification accuracy of 91.33% and 91.03% respectively. Moreover, SVM consumed 36% more time than ANN or classification.

  Language:MATLAB91

• git2cchen/KSTTM

  Matlab Codes for Kernelized Support Tensor Train Machines (KSTTM)

  Language:MATLAB88

• cdfbdex/pySTMM

  Python 3 Implementation for Support Tensor Machine Multiclassifier

  Language:Python7201

• mtanveer1/Pinball-Loss-Twin-Support-Vector-Clustering

  Language:MATLAB71

• Anomaly-Detection-paper/SR-CNN

  Time-Series Anomaly Detection

  Language:Jupyter Notebook5102

• homarques/SDS-hyperparameter-selection

  Hyperparameter selection of one-class support vector machine by self-adaptive data shifting

  Language:MATLAB3102

• PeterLiPeide/TEC_Tensor_Ensemble_Classifier

  Language:Python310

• sanchayan721/SupportVectorMachineVsKNN

  Support Vector Machine : Theoretical Development with loss function and K-Fold Cross Validation

  Language:HTML33

• Anomaly-Detection-paper/Learning-Deep-Features-for-One-Class-Classification

  Anomaly detection for deep SVDD

  Language:Jupyter Notebook2100

• Anomaly-Detection-paper/Alternative_algorithms_for_deep_SVDD

  master's thesis

  Language:Jupyter Notebook10

• vijmanan04/SVM_-Clustering-Machine_Learning

  Exploration of K-means Support Vector Machine AI Algortihm

  Language:MATLAB110