Tags: machine learning

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  1. High Temperature Oxide Property Explorer

    29 Jun 2020 | Contributor(s):: Zachary D McClure, Alejandro Strachan

    Explore material properties of common and niche oxide materials for high-temperature applications

  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. Hands-on Deep Learning for Materials

    10 Jun 2020 | | Contributor(s):: Saaketh Desai, Edward Kim

    This tool introduces users to deep learning techniques such as convolutional neural networks and variational auto encoders from a materials standpoint

  4. ECE 595ML: Course Overview

    28 May 2020 | | Contributor(s):: Stanley H. Chan

  5. ECE 595ML Lecture 1.2: Linear Regression - Geometry

    28 May 2020 | | Contributor(s):: Stanley H. Chan

  6. Jon Nykiel

    I'm Jon Nykiel, a fourth year undergraduate studying Materials Science and Applied Physics at the Ohio State University. I'm participating in NCN's SCALE URE program with Dr. Strachan of Purdue...

    https://nanohub.org/members/288810

  7. Rebecca Mosier

    Rebecca Mosier is a second-year undergraduate student at Johns Hopkins University. Her majors are Biomedical Engineering and Applied Mathematics & Statistics. She is working on the Data-Driven...

    https://nanohub.org/members/288446

  8. 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...

  9. Hands-on Unsupervised Learning using Dimensionality Reduction via Matrix Decomposition (2nd offering)

    30 Apr 2020 | | Contributor(s):: Michael N Sakano, Alejandro Strachan

    This tutorial introduces unsupervised machine learning algorithms through dimensionality reduction via matrix decomposition techniques in the context of chemical decomposition of reactive materials in a Jupyter notebook on nanoHUB.org. The tool used in this demonstration...

  10. 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...

  11. PennyLane - Automatic Differentiation and Machine Learning of Quantum Computations

    29 Apr 2020 | | Contributor(s):: Nathan Killoran

    PennyLane is a Python-based software framework for optimization and machine learning of quantum and hybrid quantum-classical computations.

  12. Nathan Killoran

    Nathan holds a MSc in Mathematics from the University of Toronto and a PhD in Physics from the University of Waterloo. He specializes in quantum computing, deep learning, and quantum optics.

    https://nanohub.org/members/286348

  13. Machine Learning for Chemical Sensing

    29 Apr 2020 | | Contributor(s):: Bruno Ribeiro

  14. Hands-on Unsupervised Learning using Dimensionality Reduction via Matrix Decomposition (1st offering)

    29 Apr 2020 | | Contributor(s):: Michael N Sakano, Alejandro Strachan

    This tutorial introduces unsupervised machine learning algorithms through dimensionality reduction via matrix decomposition techniques in the context of chemical decomposition of reactive materials in a Jupyter notebook on nanoHUB.org. The tool used in this demonstration...

  15. Hands-on Sequential Learning and Design of Experiments

    29 Apr 2020 | | Contributor(s):: Juan Carlos Verduzco Gastelum, Alejandro Strachan

    This tutorial introduces the concept of sequential learning and information acquisition functions and how these algorithms can help reduce the number of experiments required to find an optimal candidate. A hands-on approach is presented to optimize the ionic conductivity of ceramic...

  16. 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...

  17. 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...

  18. 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:...

  19. 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...

  20. Introduction to Jupyter Notebooks, Data Organization and Plotting (1st offering)

    21 Apr 2020 | | Contributor(s):: Juan Carlos Verduzco Gastelum, Alejandro Strachan

    This tutorial gives an introductory demonstration of how to create and use Jupyter notebooks. It showcases the libraries Pandas to manipulate and organize data with functionalities similar to those of Excel on python, and Plotly, a library used to create interactive plots for enhanced...