The tutorials in this NISS series involve the Top 10 analytics approaches of the key topics that are used in business today! Students and faculty, these are perhaps the top ten most important and practical topics that may not be covered in your program of study. (Review the Overview Presentation about all 10 Sessions).
In the past a few years, deep learning has gained traction in many application areas especially when non-traditional data sources are involved such as text and image. Deep learning methods now become essential tools in data scientist’s toolbox. In this tutorial, we will first introduce general concepts in deep learning using feed forward neural network (FFNN). Then we will cover convolutional neural network (CNN) for image related applications, and recurrent neural network (RNN) for text related applications. The purpose of this tutorial is introduction of fundamental concepts and applications of deep learning. We will focus on the application part with hands on exercises to build deep learning models for two well-known datasets: handwritten digit image dataset and IMDB movie review dataset. If you are a statistician and data scientist with very limited experience or knowledge of deep learning, this is the right place for you to jump start your deep learning application skills. After taking this course, you will be able to apply FFNN, CNN and RNN methods to your day-to-day work to combine structured dataset of numerical and categorical features with unstructured dataset of text and image.
We will provide pdf presentation hands out, Databrick R notebooks with examples of FFNN, CNN and RNN with embedded data of text (IMDB movie reviews) and image (hand written digit images). We will also demo how to import and run these R notebooks in Databrick and you can repeat these R notebooks in your own Databrick community account which is free to apply and use for educational purposes.
UPDATE! Materials for the Tutorial Session - NISS-Deep-Learning-Tutorial.pdf
Ming Li (Senior Research Scientist at Amazon)
NISS is interested in sharing knowledge. To this end, these webinars have been geared to provide practical information that you can use tomorrow. Examples, projects and code sharing are a part of these sessions wherever possible.
Participants require a working knowledge of probability distributions, statistical inference, statistical modeling and time series analysis as a prerequisite. Students who do not have this foundation or have not reviewed this material within the past couple of years will struggle with the concepts and methods that build on this foundation.
Select a registration/payment option above the 'Register for this Event' button ($35 for this Data Science Essentials tutorial session, $250 for all 10 Essential Data Science for Business tutorial sessions. Can attend this live session? Post Session Access to tutorial materials and recording can be obtained for $35 after the event is over.). NISS Affiliates, (https://www.niss.org/affiliates-list), please send an email to firstname.lastname@example.org.). Notifications: You will recieve an email that comes immediately to let you know you paid. Links to the event will come via email the day before and one hour prior to the actual session.
About the Instructor
Ming Li is currently a senior research scientist at Amazon and adjunct faculty of University of Washington. He organized and presented 2018 JSM Introductory Overview Lecture: Leading Data Science: Talent, Strategy, and Impact. He was the Chair of Quality & Productivity Section of ASA. He was a Data Scientist at Walmart and a Statistical Leader at General Electric Global Research Center before joining Amazon. He obtained his Ph.D. in Statistics from Iowa State University in 2010. With a deep statistics background and a few years’ experience in data science and machine learning, he has trained and mentored numerous junior data scientists with different backgrounds such as statistician, programmer, software developer, database administrator and business analyst. He is also an Instructor of Amazon’s internal Machine Learning University and was one of the key founding members of Walmart’s Analytics Rotational Program.