Tags: Jupyter notebooks

All Categories (1-20 of 155)

  1. Autonomous Neutron Diffraction Explorer

    01 Nov 2021 | | Contributor(s):: Austin McDannald

    Autonomously control neutron diffraction experiments to discover order parameter.

  2. Teaching and Learning with Jupyter

    Collections | 30 Aug 2021 | Posted by Tanya Faltens

    https://nanohub.org/members/29294/collections/python-and-jupyter-notebooks

  3. Incorporating Notebooks in STEM Classes and Deploying Interactive Applications

    10 Aug 2021 | | Contributor(s):: Rei Sanchez-Arias

    In this talk we discuss how notebooks can be introduced as working examples for hands-on classwork activities in different courses. We also briefly discuss the development and deployment of interactive applications useful for data exploration, analysis, and reporting, that users can host as...

  4. Learning Python 3 using "Python for Everybody" with nanoHUB Jupyter Notebooks

    06 Jul 2021 | | Contributor(s):: Tanya Faltens

    This document provides instructions for setting up your nanoHUB file structure and using Jupyter notebooks in nanoHUB to go through the online course, "Python for Everybody".

  5. The Jupyter Dashboard, Notebook and Terminal Interfaces in nanoHUB

    06 Jul 2021 | | Contributor(s):: Tanya Faltens

    This document describes the functionalities and appearance of the Jupyter Dashboard, Notebook and Terminal interfaces in nanoHUB.

  6. Getting Started with Python and Jupyter Notebooks in nanoHUB

    06 Jul 2021 | | Contributor(s):: Tanya Faltens

    These tutorials will introduce you to using Jupyter notebooks in nanoHUB and provide you with foundational information that will support learning coding and using more advanced Jupyter notebook tools and apps that are published in nanoHUB.

  7. Bayesian optimization tutorial using Jupyter notebook

    11 Jun 2021 | | Contributor(s):: Hieu Doan, Garvit Agarwal

    Active learning via Bayesian optimization for materials discovery

  8. IPython Notebooks for Machine Learning

    Collections | 21 May 2021 | Posted by Tanya Faltens

    https://nanohub.org/members/29294/collections/ncn-ure

  9. Jupyter Notebook with Anaconda 2020.11

    21 May 2021 | | Contributor(s):: Steven Clark

    Starts the Jupyter notebook server in your home directory.

  10. Modeling and visualization of geometric errors for hemisphere structures produced by two-photon lithography

    15 Jan 2021 | | Contributor(s):: Sixian Jia, Yuhang Yang, Varun Ajit Kelkar, Hemangg Singh Rajput, Adriana Carola Salazar Coariti, Kimani C Toussaint, Chenhui Shao

    3D geometric measurements of fabricated structures/features

  11. Teaching Engineering using Jupyter Notebooks

    29 Dec 2020 | | Contributor(s):: Susan P Gentry, Rei Sanchez-Arias, David R. Ely, Jon Nykiel, Cindy Nguyen

    This talk discusses the use of Jupyter Notebooks on nanoHUB for teaching materials engineering.

  12. Cycle Training App for PhysiCell

    18 Dec 2020 | | Contributor(s):: Furkan Kurtoglu, Aneequa Sundus, Kali Nicole Konstantinopoulos, Drew Willis, Mary Chen, Randy Heiland, Paul Macklin

    Training application for "Cycle" concept in PhysiCell.

  13. Jupyter in nanoHUB: Developing and Deploying Jupyter Tools in nanoHUB

    16 Dec 2020 | | Contributor(s):: Alejandro Strachan

    This presentation is available for pre-screening. The final presentation production will be forth coming.

  14. Hands-On Data Science and Machine Learning in Undergraduate Education

    07 Oct 2020 | | Contributor(s):: Alejandro Strachan, Saaketh Desai, Juan Carlos Verduzco Gastelum, Michael N Sakano, Zachary D McClure, Joseph M. Cychosz, Jared Gray West

    This series of modules introduce key concepts in data science in the context of application in materials science and engineering.

  15. Module 5: 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...

  16. Module 4: 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. We will apply liner regression to predict materials properties and to explore correlations between materials properties via hands-on online simulations. Linear regression is a supervised machine learning...

  17. nanoHUB: Online Simulation and Data

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

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

  18. 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.

  19. 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.

  20. 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