mutual-ai Goto Github PK

cortexsys

Matlab and Octave GPU Accelerated Deep Learning Toolbox

cosmomvpa

A lightweight multivariate pattern analysis (MVPA) toolbox in Matlab / Octave

course_statistics_story

天津医科大学，本科选修课，《故事中的统计学》课程资料

coursera-ml

Machine Learning with Coursera

coursera-ml-andrewng-notes

吴恩达老师的机器学习课程个人笔记

covariancetoolbox

Covariance toolbox for matlab, including riemannian geometry

cox-nnet

cpac

Pytorch implementation of our article "Clustering-driven Deep Embedding with Pairwise Constraints"

cs229

Stanford CS229 (Autumn 2017)

cs231n.github.io

Public facing notes page

cs273a-introduction-to-machine-learning

Introduction to machine learning and data mining How can a machine learn from experience, to become better at a given task? How can we automatically extract knowledge or make sense of massive quantities of data? These are the fundamental questions of machine learning. Machine learning and data mining algorithms use techniques from statistics, optimization, and computer science to create automated systems which can sift through large volumes of data at high speed to make predictions or decisions without human intervention. Machine learning as a field is now incredibly pervasive, with applications from the web (search, advertisements, and suggestions) to national security, from analyzing biochemical interactions to traffic and emissions to astrophysics. Perhaps most famously, the $1M Netflix prize stirred up interest in learning algorithms in professionals, students, and hobbyists alike. This class will familiarize you with a broad cross-section of models and algorithms for machine learning, and prepare you for research or industry application of machine learning techniques. Background We will assume basic familiarity with the concepts of probability and linear algebra. Some programming will be required; we will primarily use Matlab, but no prior experience with Matlab will be assumed. (Most or all code should be Octave compatible, so you may use Octave if you prefer.) Textbook and Reading There is no required textbook for the class. However, useful books on the subject for supplementary reading include Murphy's "Machine Learning: A Probabilistic Perspective", Duda, Hart & Stork, "Pattern Classification", and Hastie, Tibshirani, and Friedman, "The Elements of Statistical Learning".

csc321_notes

& homework

cse591-practicals

Codes for ASU, CSE591, Spring 2017.

ctex

cuda

GPU-accelerated LIBSVM is a modification of the original LIBSVM that exploits the CUDA framework to significantly reduce processing time while producing identical results. The functionality and interface of LIBSVM remains the same. The modifications were done in the kernel computation, that is now performed using the GPU.

cuda-1

CUDA examples.

cudahandbook

Source code that accompanies The CUDA Handbook.

curatedbreastcancer

15 studies encompassing 24 different datasets (matrices) from GEO breast cancer gene expression experiments. Treatment information and outcomes data is also curated for each patient (datasets were carefully chosen based upon whether I could determine each sample/patient's treatment regimen, and whether the samples had at least one outcomes variable measured.) This totals over 2700 samples all together from large-scale studies and clinical trials. These datasets are available as an R package, curatedBreastData.

curricularface

CurricularFace(CVPR2020)

curvature

Curvature descriptor for image processing

cutpointr

Optimal cutpoints in R: determining and validating optimal cutpoints in binary classification

cvpr16-deepbit

Learning Compact Binary Descriptors with Unsupervised Deep Neural Networks (CVPR16)

cvpr17

CVPR 2017 Tutorial

cvpr2016

Paper about face detection, landmark detection, recognition, reconstruction, text detection and so on.

cvpr2019

cvpr2019 papers

cvprtoolbox

Yet Another MATLAB Computer Vision and Pattern Recognition toolbox

cvx

A MATLAB system for disciplined convex programming

cvx_short_course

Materials for a short course on convex optimization.

cvxflow

cvxpy

A Python-embedded modeling language for convex optimization problems.