Tags: NCN Group - Machine Learning

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  1. 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...

  2. 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...

  3. 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...

  4. 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:...

  5. 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...

  6. 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...

  7. Introduction to Jupyter Notebooks, Data Organization and Plotting (2nd 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...

  8. Machine Learning Workshop for Materials Science

    27 Jan 2020 | | Contributor(s):: Saaketh Desai

    This workshop covers the fundamentals of machine learning and data science, with a focus on material science applications. This workshop includes a hands-on demonstration of the nanoHUB tool Machine Learning for Materials Science: Part 1.

  9. ECE 595ML: Machine Learning I

    17 Jan 2020 | | Contributor(s):: Stanley H. Chan

    Spring 2020 - This course is in productionCourse Website: https://engineering.purdue.edu/ChanGroup/ECE595/index.htmlCourse Outline:Part 1: Mathematical BackgroundLinear Regression and OptimizationPart 2: ClassificationMethods to train linear classifiersFeature analysis, Geometry, Bayesian...

  10. Citrine Tools for Materials Informatics

    05 Dec 2019 | | Contributor(s):: Juan Carlos Verduzco Gastelum, Alejandro Strachan

    Jupyter notebooks for sequential learning in the context of materials design. Run your own models, explore various methods and adapt the notebooks to your needs.

  11. 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

  12. 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