Tags: Jupyter notebooks

All Categories (1-20 of 140)

  1. Neural Networks for Regression and Classification

    01 Oct 2020 | | Contributor(s):: Saaketh Desai, Alejandro Strachan

    This module introduces neural networks for material science and engineering with hands-on online simulations. Neural networks are a subset of machine learning models used to learn mappings between inputs and outputs for a given dataset. Neural networks offer great flexibility and have shown great...

  2. Linear Regression Models

    01 Oct 2020 | | Contributor(s):: Michael N Sakano, Saaketh Desai, Alejandro Strachan

    This module introduces linear regression in the context of materials science and engineering.

  3. nanoHUB: Online Simulation and Data

    24 Sep 2020 | | Contributor(s):: Alejandro Strachan

    These slides introduce nanoHUB, an open platform for online simulations and collaboration.

  4. COVID-19 data analysis

    07 Aug 2020 | | Contributor(s):: Randy Heiland, Paul Macklin

    Perform data analysis in a Jupyter notebook using data from the pc4covid19 tool.

  5. ECG Data Analysis Using Machine Learning

    03 Aug 2020 | | Contributor(s):: Rebecca Mosier, Guang Lin

    Perform data analysis on ECG data using machine learning methods.

  6. Interactive Learning Tools for Scientific Computing and Data Analysis Using R

    29 Jul 2020 | | Contributor(s):: Cindy Nguyen, Rei Sanchez-Arias

    Root-finding methods and numerical optimization techniques with applications in science, engineering, and data analysis

  7. Data Analysis of Normal Data Sets in Engineering

    24 Jul 2020 | | Contributor(s):: Joseph Joshua Williams, Nancy Ruzycki

    Statistical and data analysis concepts in engineering

  8. Matlab Data Analysis Using Jupyter Notebooks

    24 Jul 2020 | | Contributor(s):: Jon Nykiel, Anna Leichty, Zachary D McClure, Alejandro Strachan, Aileen Ryan, Adrian Nat Gentry, Amanda Johnston, Tamara Jo Moore, Allen Garner, Peter Bermel

    Use Jupyter Notebooks with a Matlab kernel running in the background for data analysis and intro to engineering homework problems

  9. Refractory Complex Concentrated Alloy Melting Point Calculation

    28 May 2020 | | Contributor(s):: Zachary D McClure, Saaketh Desai, Alejandro Strachan

    Calculate melting point of BCC-type high entropy alloys through phase coexistence method

  10. 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

  11. 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

  12. PhysiBoSSa: cell fate decision in TNF Boolean model

    13 May 2020 | | Contributor(s):: Gerard Pradas, Arnau Montagud, Miguel Ponce de Leon

    PhysiBoSSa model of the cell fate decision in TNF Boolean model in a multicellular multiscale system

  13. XRD interactive trends plot

    11 May 2020 | | Contributor(s):: Enze Chen

    Observe changes in powder XRD spectra by modifying experimental parameters.

  14. Running a Python 3 Script in a nanoHUB Jupyter Notebook

    01 May 2020 | | Contributor(s):: Tanya Faltens

    This tutorial will show you how to create and run Python 3 code in a Jupyter notebook, rather than creating and running a Python script. We are working along with Chapter 1.8 “Writing a program” in the Python for Everybody course. In this lesson they execute a Python script that...

  15. 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...

  16. 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...

  17. 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...

  18. 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...

  19. 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:...

  20. Repositories and Data Management (2nd offering)

    22 Apr 2020 | | Contributor(s):: Zachary D McClure, Alejandro Strachan

    This tutorial introduces database infrastructure and APIs for performing different scales of querying. You will learn how to access different suites of information from three prominent databases, and some advanced examples of data visualization and processing will be discussed. The Querying Data...