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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
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...
Creating Inflections: DARPA’s Electronics Resurgence Initiative
09 Jan 2019 | | Contributor(s):: William Chappell
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.
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
Juan Carlos Verduzco Gastelum
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...
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...
Jeremy Seiji Marquardt
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...
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...
S Kiran Kadam
IPython Notebooks for Machine Learning
21 May 2017 |
Posted by Tanya Faltens
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