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Apr 16 2020
Supervised learning part 2: classification and random forests
Topics covered in this session:Classification using neural networksDeveloping and training random forest modelsOrganizers: Alejandro Strachan, Saaketh DesaiLeader: Saaketh DesaiRegister for this...
https://nanohub.org/events/details/1849
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Apr 15 2020
Supervised learning part 2: classification and random forests
Topics covered in this session:Classification using neural networksDeveloping and training random forest modelsOrganizers: Alejandro Strachan, Saaketh DesaiLeader: Saaketh DesaiRegister for this...
https://nanohub.org/events/details/1843
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Apr 14 2020
Supervised learning part 1: linear regression and neural networks
Topics covered in this session:Simple regressionDeveloping and training neural networksOrganizers: Alejandro Strachan, Saaketh DesaiLeader: Saaketh DesaiRegister for this seminarHands-on data...
https://nanohub.org/events/details/1848
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Apr 13 2020
Supervised learning part 1: linear regression and neural networks
Topics covered in this session:Simple regressionDeveloping and training neural networksOrganizers: Alejandro Strachan, Saaketh DesaiLeader: Saaketh DesaiRegister for this seminarHands-on data...
https://nanohub.org/events/details/1842
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Toward a Thinking Microscope: Deep Learning-Enabled Computational Microscopy and Sensing
29 Jan 2020 | | Contributor(s):: Aydogan Ozcan
In this presentation, I will provide an overview of some of our recent work on the use of deep neural networks in advancing computational microscopy and sensing systems, also covering their biomedical applications.
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MSEML: Machine Learning for Materials Science Tool on nanoHUB
27 Jan 2020 | | Contributor(s):: Saaketh Desai
This talk is a hands-on demonstration using the nanoHUB tool Machine Learning for Materials Science: Part 1.
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Data Science and Machine Learning for Materials Science
22 Jan 2020 | | Contributor(s):: Saaketh Desai
This talk covers the fundamentals of machine learning and data science, focusing on material science applications. The talk is for a general audience, attempting to introduce basic concepts such as linear regression, supervised learning with neural networks including forward and back...
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3 min Research Talk: Hierarchical Material Optimization using Neural Networks
29 Oct 2019 | | Contributor(s):: Miguel Arcilla Cuaycong
In this presentation, we sought to use a neural network (NN) to identify optimal arrangements of four different constituents in a tape spring to be used as snapping mechanisms in phase transforming cellular material that can dissipate energy.
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Hierarchical material optimization
28 Oct 2019 | | Contributor(s):: Miguel Arcilla Cuaycong
Assembles all possible configurations of a structural level in a Hierarchical Material.
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Hierarchical Material Optimization using Neural Networks
01 Aug 2019 | | Contributor(s):: Miguel Arcilla Cuaycong, Valeria Grillo, Kristiaan William Hector, Pablo Daniel Zavattieri
Material structures that occur in nature are commonly made up of complex architectures arranged in a hierarchy. These hierarchical architectures are made up of different structural levels consisting of a unique arrangement of simple constituents, acting as building blocks, that satisfy a local...
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Big Data in Reliability and Security: Some Basics
30 May 2019 | | Contributor(s):: Saurabh Bagchi
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Big Data in Reliability and Security: Applications
30 May 2019 | | Contributor(s):: Saurabh Bagchi
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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
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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...
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How can I simulate a Memristive Neural Network to detect Edges?
Q&A|Closed | Responses: 0
I am trying to build a Memristive Neural Network that can detect edges in a given image.
I am currently using LTSpice to simulate the Memristive crossbar, and MATLAB to write the...
https://nanohub.org/answers/question/2106
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Networked Dynamical Systems for Function and Learning: Paradigms for Data-Driven Control and Learning in Neurosensory Systems
16 Jan 2019 | | Contributor(s):: J. Nathan Kutz
Our objective is to use emerging data-driven methods to extract the underlying engineering principles of cognitive capability, namely those that allow complex networks to learn and enact control and functionality in the robust manner observed in neurosensory systems. Mathematically, the...
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Imaging Sciences at the Oak Ridge National Laboratory: Identity Sciences, Advanced Manufacturing, Computational Imaging, Machine Learning, and Super Computing
03 Jan 2019 | | Contributor(s):: Hector J. Santos-Villalobos
Dr. Santos takes us on the journey of working at the Oak Ridge National Laboratory as an imaging scientist. He showcases work in the areas of Identity Sciences (i.e., biometrics), Machine Learning, and Computational Imaging. Some application to discuss are coded source neutron imaging,...
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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
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Magnetic Tunnel Junction (MTJ) as Stochastic Neurons and Synapses: Stochastic Binary Neural Networks, Bayesian Inferencing, Optimization Problems
26 Oct 2018 | | Contributor(s):: Abhronil Sengupta, Kaushik Roy
In this presentation, we provide a multi-disciplinary perspective across the stack of devices, circuits, and algorithms to illustrate how the stochastic switching dynamics of spintronic devices in the presence of thermal noise can provide a direct mapping to the units of such computing...
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Re-Engineering Computing For Next Generation Autonomous Intelligent Systems: Devices, Circuits, and Algorithms
27 Aug 2018 | | Contributor(s):: Kaushik Roy, Abhronil Sengupta
Advances in machine learning, notably deep learning, have led to computers matching or surpassing human performance in several cognitive tasks including vision, speech and natural language processing. However, implementation of such neural algorithms in conventional "von-Neumann"...