Tags: Machine Learning

All Categories (1-20 of 24)

  1. Muhammad Bilal

    https://nanohub.org/members/179709

  2. Applying Machine Learning to Computational Chemistry: Can We Predict Molecular Properties Faster without Compromising Accuracy?

    14 Aug 2017 | Presentation Materials | Contributor(s): Hanjing Xu, Pradeep Kumar Gurunathan

    Non-covalent interactions are crucial in analyzing protein folding and structure, function of DNA and RNA, structures of molecular crystals and aggregates, and many other processes in the fields...

    https://nanohub.org/resources/26904

  3. Predicting Locations of Pollution Sources using Convolutional Neural Networks

    07 Aug 2017 | Presentation Materials | Contributor(s): Yiheng Chi, Guang Lin, Nickolas D Winovich

    Pollution is a severe problem today, and the main challenge in water pollution controls and eliminations is detecting and locating pollution sources. This research project aims to predict the...

    https://nanohub.org/resources/26948

  4. S Kiran Kadam

    https://nanohub.org/members/172030

  5. IPython Notebooks for Machine Learning

    Collections | 21 May 2017 | Posted by Tanya Faltens

    https://nanohub.org/groups/ncnure2017/collections/technical-resources

  6. Dedy Farhamsa

    https://nanohub.org/members/170299

  7. Claire Battye

    https://nanohub.org/members/169180

  8. Marius Stan

    https://nanohub.org/members/156713

  9. Model Selection Using Gaussian Mixture Models and Parallel Computing

    27 Jul 2016 | Tools | Contributor(s): Tian Qiu, Yiyi Chen, Georgios Karagiannis, Guang Lin

    Model Selection Using Gaussian Mixture Models

    https://nanohub.org/resources/msugmmpc

  10. Lukasz Burzawa

    https://nanohub.org/members/147258

  11. Juan Sebastian Martinez

    I am a senior in Electronic Engineering and Systems and Computer Engineering at Universidad de los Andes in Bogotá. Throughout my learning, I have gained experience with different programming...

    https://nanohub.org/members/145729

  12. Gaussian process regression in 1D

    04 Dec 2014 | Tools | Contributor(s): Ilias Bilionis, Alejandro Strachan, Benjamin P Haley, Martin Hunt, Rohit Kaushal Tripathy, Sam Reeve

    Use Gaussian processes to represent x-y data

    https://nanohub.org/resources/gptool

  13. Ilias Bilionis

    Dr. Ilias Bilionis is an Assistant Professor at the School of Mechanical Engineering, Purdue University. His research is motivated by energy and material science applications and it focuses on the...

    https://nanohub.org/members/107467

  14. Rohit Kaushal Tripathy

    https://nanohub.org/members/106614

  15. German Felipe Giraldo

    https://nanohub.org/members/85538

  16. Ahmed-Amine Homman

    https://nanohub.org/members/82074

  17. anupam ghosh

    M.Sc. Physics (2007), 1 year experience in neuroscience (2008-09), 1.5 yrs experience in synthesis and characterization of Nickel nano-wires (2010-11), 1 year experience in simulation of...

    https://nanohub.org/members/81944

  18. Mahika Dubey

    University of Illinois Urbana Champaign Class of 2016 (Urbana, IL) B.S. Computer Engineering (Department of ECE) Minor in Statisics, iFoundry Innovation Certificate Program Monta Vista High School...

    https://nanohub.org/members/80897

  19. Random Forest Model Objects for Pulmonary Toxicity Risk Assessment

    17 Apr 2013 | Downloads | Contributor(s): Jeremy M Gernand

    This download contains MATLAB treebagger or Random Forest (RF) model objects created via meta-analysis of nanoparticle rodent pulmonary toxicity experiments. The ReadMe.txt file contains object...

    https://nanohub.org/resources/17539

  20. Model Validation Document for "A Meta-Analysis of Carbon Nanotube Pulmonary Toxicity Studies – How Physical Dimensions and Impurities Affect the Toxicity of Carbon Nanotubes"

    19 Nov 2012 | Papers | Contributor(s): Jeremy M Gernand, Elizabeth Casman

    This document contains model learning statistics, and structure of the models utilized in the paper “A meta-analysis of carbon nanotube pulmonary toxicity studies – How physical dimensions and...

    https://nanohub.org/resources/15901