-
Hands-on Supervised Learning: Part 2 - Classification and Random Forests (1st offering)
Online Presentations | 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...
-
Hands-on Supervised Learning: Part 1 - Linear Regression and Neural Networks
Online Presentations | 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:...
-
Hands-on Data Science and Machine Learning Training Series
Courses | 21 Apr 2020 | Contributor(s):: Alejandro Strachan, Saaketh Desai, Arun Kumar Mannodi Kanakkithodi
his series of workshops introduces participants to important concepts and techniques in data science and machine learning in the context engineering and physical sciences applications. All workshops include hands-on activities.
-
Introduction to Jupyter Notebooks, Data Organization and Plotting (1st offering)
Online Presentations | 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...
-
Introduction to Jupyter Notebooks, Data Organization and Plotting (2nd offering)
Online Presentations | 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...
-
Unsupervised learning using dimensionality reduction via matrix decomposition
Tools | 14 Apr 2020 | Contributor(s):: Michael N Sakano, Alejandro Strachan
Learn PCA and NMF via chemistry example
-
Apr 09 2020
Intro to Jupyter in nanoHUB, Pandas for data organization and plotting
Topics covered in this session:Using Jupyter notebooks on nanoHUBOrganizing data using Pandas and simple plotting with PlotlyOrganizers: Alejandro Strachan, Saaketh DesaiLeader: Juan Carlos...
https://nanohub.org/events/details/1847
-
Apr 08 2020
Intro to Jupyter in nanoHUB, Pandas for data organization and plotting
Topics covered in this session:Using Jupyter notebooks on nanoHUBOrganizing data using Pandas and simple plotting with PlotlyOrganizers: Alejandro Strachan, Saaketh DesaiLeader: Juan Carlos...
https://nanohub.org/events/details/1840
-
Asep Ridwan Setiawan
https://nanohub.org/members/282014
-
Marco Paul Apolinario Lainez
https://nanohub.org/members/278717
-
Toward a Thinking Microscope: Deep Learning-Enabled Computational Microscopy and Sensing
Online Presentations | 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.
-
Introduction to Machine Learning in MSE: Predicting Bulk Modulus
Tools | 29 Jan 2020 | Contributor(s):: Adrian Nat Gentry, Peilin Liao
In this module, you will learn how to predict bulk modulus using machine learning.
-
Machine Learning Workshop for Materials Science
Workshops | 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.
-
MSEML: Machine Learning for Materials Science Tool on nanoHUB
Online Presentations | 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.
-
Data Science and Machine Learning for Materials Science
Online Presentations | 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...
-
ECE 595ML Lecture 1.1: Linear Regression
Online Presentations | 21 Jan 2020 | Contributor(s):: Stanley H. Chan
-
ECE 595ML Lecture 2.1: Regularized Linear Regression
Online Presentations | 21 Jan 2020 | Contributor(s):: Stanley H. Chan
-
ECE 595ML: Machine Learning I
Courses | 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...
-
ECE 595ML: Introduction
Online Presentations | 17 Jan 2020 | Contributor(s):: Stanley H. Chan
-
Stanley H. Chan
Stanley H. Chan is currently an assistant professor in the School of Electrical and Computer Engineering and the Department of Statistics at Purdue University.Dr. Chan received the Ph.D. degree in...
https://nanohub.org/members/275407