Rishabh Iyer

Slides and other material (e.g. Demos) of my ML Courses CS 6375 and CS 4375 at UT Dallas

CS 7301: Spring 2021 Course on Advanced Topics in Optimization in Machine Learning

Material for my course: Optimization in Machine Learning

Some simple demos I use in my optimization in ML course. Includes implementations of ML loss functions (Logistic Loss, SVM Loss, ..) and optimization algorithms (gradient descent, accelerated variants, conjugate GD, etc.)

95.47% on CIFAR10 with PyTorch

Online Learning built on top of Jensen

A C++ toolkit for Convex Optimization (Logistic Loss, SVM, SVR, Least Squares etc.), Convex Optimization algorithms (LBFGS, TRON, SGD, AdsGrad, CG, Nesterov etc.) and Classifiers/Regressors (Logistic Regression, SVMs, Least Squares Regression etc.)

CORDS: COResets and Data Subset selection

A web app for ranking computer science departments according to their research output in selective venues.

Caffe: a fast open framework for deep learning.

Open Source Computer Vision Library

No description provided.