Tags: Machine Learning

All Categories (1-20 of 36)

  1. Machine Learning for Materials Science: Part 1

    09 Feb 2019 | Contributor(s):: Juan Carlos Verduzco Gastelum, Alejandro Strachan, Saaketh Desai

    Machine learning and data science tools applied to materials science

  2. 3 min Research Talk: Deep Machine Learning for Machine Performance & Damage Prediction

    04 Feb 2019 | | Contributor(s):: Elijah Reber

    In this talk, we look at how effective a deep neural network is at predicting the failure or energy output of a wind turbine. A data set was obtained that contained sensor data from 17 wind turbines over 13 months, measuring numerous variables, such as spindle speed and blade position and whether...

  3. Creating Inflections: DARPA’s Electronics Resurgence Initiative

    09 Jan 2019 | | Contributor(s):: William Chappell

  4. ECE 695E: An Introduction to Data Analysis, Design of Experiment, and Machine Learning

    07 Jan 2019 | | Contributor(s):: Muhammad A. Alam

    This course will provide the conceptual foundation so that a student can use modern statistical concepts and tools to analyze data generated by experiments or numerical simulation.

  5. TensorFlow Tutorials

    03 Dec 2018 | | Contributor(s):: Juan Carlos Verduzco Gastelum, Saaketh Desai, Alejandro Strachan

    Ready-to-run Jupyter notebooks for machine learning using Tensorflow and Keras

  6. Desmond Brennan

    Providing dissertation help at University of Florida

    http://nanohub.org/members/214385

  7. Juan Carlos Verduzco Gastelum

    Materials Engineering PhD Graduate Student at Purdue University.Research in "Solid-state energy storage devices rational materials design".Background in Mechanical and Electrical Engineering.

    http://nanohub.org/members/207041

  8. Deep Machine Learning for Machine Performance and Damage Prediction

    08 Aug 2018 | | Contributor(s):: Elijah Reber, Nickolas D Winovich, Guang Lin

    Deep learning has provided opportunities for advancement in many fields. One such opportunity is being able to accurately predict real world events. Ensuring proper motor function and being able to predict energy output is a valuable asset for owners of wind turbines. In this paper, we look at...

  9. Is More Data Better Than Better Algorithms in Machine Learning?

    08 Jun 2018 | Posted by Cogito Tech LLC

    Yes in machine learning more data is always better than better algorithms. Actually, the quality of data defines how the inputs will work in machine learning training and output would be exactly...

    http://nanohub.org/members/200897/blog/2018/06/is-more-data-better-than-better-algorithms-in-machine-learning

  10. Jeremy Seiji Marquardt

    Sophomore going on Junior in Nuclear Engineering Bachelors, with interest in extreme nuclear materials and a minor in math. Currently participating in SURF at nanoHUB with Prof. Koslowski's group.

    http://nanohub.org/members/199533

  11. Patrick Heney

    I started programming on a TI‑99/4A when I was about 8 years old.I learned how to build computers during an internship with a technology company in Washington DC.I started my career in the...

    http://nanohub.org/members/194443

  12. Muhammad Bilal

    Bilal’s research focuses on data-driven solutions for the environmental and health impact assessment of engineered nanomaterials (ENMs) using advanced machine learning/data mining and simulation...

    http://nanohub.org/members/179709

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

    14 Aug 2017 | | 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 of biology and chemistry. However, it is time and resource consuming to calculate such interactions...

  14. Predicting Locations of Pollution Sources using Convolutional Neural Networks

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

    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 locations of pollution sources given diffusion information of pollution in the form of array or...

  15. S Kiran Kadam

    http://nanohub.org/members/172030

  16. IPython Notebooks for Machine Learning

    Collections | 21 May 2017 | Posted by Tanya Faltens

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

  17. Dedy Farhamsa

    http://nanohub.org/members/170299

  18. Claire Battye

    "Research is creating new knowledge."Neil Armstrong

    http://nanohub.org/members/169180

  19. Marius Stan

    http://nanohub.org/members/156713

  20. Model Selection Using Gaussian Mixture Models and Parallel Computing

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

    Model Selection Using Gaussian Mixture Models