Tags: neural networks

Description

Neural networks are computing systems vaguely inspired by biological neural networks that as found in human or animal brains. Such systems "learn" to perform tasks by considering examples, generally without being programmed with task-specific rules.

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  1. Stochastic Computing for Brainware LSI

    29 Jun 2020 |

    This talk reviews stochastic computation and discusses the advantages and disadvantages with the recent developments in hardware. In addition, stochastic-computing based brainware LSIs (BLSIs) are introduced.

  2. SEM Image Segmentation Tutorial using SEM Image Processing Tool

    16 Jun 2020 | | Contributor(s):: Joshua A Schiller

    In this activity, students will learn about the use of image processing methods to analyze Scanning Electron Microscopy images using a technique known as Image Segmentation and the SEM Image Processing Tool. The purpose of this tutorial is demonstrate several methods for image masking:...

  3. Parsimonious Neural Networks Learn Classical Mechanics and Can Teach It

    15 May 2020 | | Contributor(s):: Saaketh Desai, Alejandro Strachan

    We combine neural networks with genetic algorithms to find parsimonious models that describe the time evolution of a point particle subjected to an external potential. The genetic algorithm is designed to find the simplest, most interpretable network compatible with the training data. The...

  4. Test Tool for Neural Network Reactive Force Field for CHNO systems

    14 May 2020 | | Contributor(s):: Pilsun Yoo, Saaketh Desai, Michael N Sakano, Peilin Liao, Alejandro Strachan

    Run molecular dynamics and Do testing using the neural network reactive force field for nitramines

  5. Hands-on Supervised Learning: Part 2 - Classification and Random Forests (2nd offering)

    30 Apr 2020 | | Contributor(s):: Saaketh Desai

    This tutorial introduces neural networks for classification tasks and random forests for regression tasks via Jupyter notebooks on nanoHUB.org. You will learn how to create and train a neural network to perform a classification, as well as how to define and train random forests. The tools used...

  6. Image Segmentation for Graphene Images

    29 Apr 2020 | | Contributor(s):: Joshua A Schiller

    This lecture outlines the need for a fast, automated means for identifying regions of images corresponding to graphene. Simple methods, like color masking and template matching, are discussed initially. Unsupervised clustering methods are then introduced as potential improvements...

  7. Hands-on Supervised Learning: Part 2 - Classification and Random Forests (1st offering)

    24 Apr 2020 | | Contributor(s):: Saaketh Desai

    This tutorial introduces neural networks for classification tasks and random forests for regression tasks via Jupyter notebooks on nanoHUB.org. You will learn how to create and train a neural network to perform a classification, as well as how to define and train random forests. The tools used...

  8. Hands-on Supervised Learning: Part 1 - Linear Regression and Neural Networks

    22 Apr 2020 | | Contributor(s):: Saaketh Desai

    This tutorial introduces supervised learning via Jupyter notebooks on nanoHUB.org. You will learn how to setup a basic linear regression in a Jupyter notebook and then create and train a neural network. The tool used in this demonstration is Machine Learning for Materials Science:...

  9. Hands-on Data Science and Machine Learning Training

    21 Apr 2020 | | Contributor(s):: Alejandro Strachan, Saaketh Desai

    This series of hands-on tutorials is designed to jump start your use of data science and machine learning in research or teaching. This series will cover the following topics:Learn how to use Jupyter notebooks for your researchInteract with data repositories and manage...

  10. 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

  11. 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

  12. 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

  13. 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

  14. 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.

  15. 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.

  16. 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...

  17. 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.

  18. Hierarchical material optimization

    28 Oct 2019 | | Contributor(s):: Miguel Arcilla Cuaycong

    Assembles all possible configurations of a structural level in a Hierarchical Material.

  19. 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...

  20. Big Data in Reliability and Security: Some Basics

    30 May 2019 | | Contributor(s):: Saurabh Bagchi