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empirical-study-of-supervised-learning-algorithms

In this project, we have implemented different supervised learning algorithms and then compared their performances. The following algorithms were implemented: 1) libSvm 2) Multiclass KSVM 3) Random Forests 4) Deep Learning (CNN, MLP) 5) Naïve Bayes 6) AdaBoost This report includes description, implementation details, performance, and results of each mentioned algorithm on given datasets. It also includes the comparison of the accuracies of the aforementioned algorithms.

end2end-all-conv

Deep Learning to Improve Breast Cancer Detection on Screening Mammography

ensemble-methods-using-r

I have done my individual project (dissertation) on ensemble methods. In which I first did the background study on different ensemble methods and then implemented Boosting, AdaBoost, Bagging and random forest techniques on underlying machine learning algorithms. I used boosting method to boost the performance of weak learner like decision stumps. Implemented bagging for decision trees (both regression and classification problems) and for KNN classifier. Used random forest for classification trees. I have implemented a special algorithm of boosting called “AdaBoost” on logistic regression algorithm using different threshold values. Then plotted the different graphs like an error rate as a function of boosting, bagging and random forest iterations. Compared results of bagging with boosting. Analysed the performance of classifier before applying ensemble methods and after applying ensemble methods. Used different model evaluation techniques like cross-validation, MSE, PRSS, ROC curves, confusion matrix, and out-of-bag error estimation to estimate the performance of ensemble techniques.

erplab

ERPLAB Toolbox is a free, open-source Matlab package for analyzing ERP data. It is tightly integrated with EEGLAB Toolbox, extending EEGLAB’s capabilities to provide robust, industrial-strength tools for ERP processing, visualization, and analysis. A graphical user interface makes it easy for beginners to learn, and Matlab scripting provides enormous power for intermediate and advanced users.

estimation-detection

Lecture notes for estimation and detection theory

eth-cam-maths

Lecture Notes in Mathematics

eth-mathematical-optimization-summary

examples

A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.

exploremodels

Code and data for "The Geometry of Classifiers"

exploring-h2o-and-tensorflow-in-r

Exploring scalability issues in R using H2O package and image classification using Tensorflow

extremely-simple-one-shot-learning

Extremely simple one-shot learning in Python

extremenn

a R implementation of extreme learning machine

eye-fundus-image-segmentation

Segmentation vasculature in retinal image

face_verification_experiment

Face verification experiment for lfw

facial-similarity-with-siamese-networks-in-pytorch

Implementing Siamese networks with a contrastive loss for similarity learning

factoextra

Extract and Visualize the Results of Multivariate Data Analyses

fancyimpute

Multivariate imputation and matrix completion algorithms implemented in Python

fashion-mnist

A MNIST-like fashion product database. Benchmark :point_right:

fast-ast

FastAST - A fast primal-dual interior point method for line spectral estimation via atomic norm soft thresholding.

fast-computation-for-distance-covariance-and-correlation

Author of All Files under this Folder: Xiaoming Huo

fastai-ml-dl-notes-zh

:book: [译] fast.ai 机器学习和深度学习中文笔记

fastbook

The fastai book, published as Jupyter Notebooks

faster

Fast Lane to Learning R!

faster-rcnn.pytorch

A faster pytorch implementation of faster r-cnn

faster_rcnn

Faster R-CNN

fastfit

Fastfit matlab toolbox

fastrpca

Matlab code for all variants of robust PCA and SPCP

fcn.berkeleyvision.org

Fully Convolutional Networks for Semantic Segmentation by Jonathan Long*, Evan Shelhamer*, and Trevor Darrell. CVPR 2015 and PAMI 2016.

featsel

An open-source framework for benchmarking of feature selection algorithms and cost functions.

feature-extraction

Computer vision feature extraction toolbox for image classification