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Name: OpenMind
Type: User
Name: OpenMind
Type: User
Matlab and Octave GPU Accelerated Deep Learning Toolbox
A lightweight multivariate pattern analysis (MVPA) toolbox in Matlab / Octave
天津医科大学,本科选修课,《故事中的统计学》课程资料
Machine Learning with Coursera
吴恩达老师的机器学习课程个人笔记
Covariance toolbox for matlab, including riemannian geometry
Pytorch implementation of our article "Clustering-driven Deep Embedding with Pairwise Constraints"
Stanford CS229 (Autumn 2017)
Public facing notes page
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".
& homework
Codes for ASU, CSE591, Spring 2017.
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 examples.
Source code that accompanies The CUDA Handbook.
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(CVPR2020)
Curvature descriptor for image processing
Optimal cutpoints in R: determining and validating optimal cutpoints in binary classification
Learning Compact Binary Descriptors with Unsupervised Deep Neural Networks (CVPR16)
CVPR 2017 Tutorial
Paper about face detection, landmark detection, recognition, reconstruction, text detection and so on.
cvpr2019 papers
Yet Another MATLAB Computer Vision and Pattern Recognition toolbox
A MATLAB system for disciplined convex programming
Materials for a short course on convex optimization.
A Python-embedded modeling language for convex optimization problems.
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.