Hands-on Workshop Deep Learning - PDH or CEU

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This full-day, in-person workshop will teach you the fundamental tools and techniques to build, train, and evaluate deep learning models. Through hands-on exercises, this workshop is designed to illustrate how deep learning works. Using examples and exercises in computer vision, you will learn to build your deep learning model from scratch and also use state-of-the-art practices such as deep transfer learning. You will also learn several hacks to improve a deep learning model and push its accuracy to the highest possible range.

Learning Objectives

  • Build, train, and evaluate convolutional neural networks using Tensorflow/Keras
  • Learn several hacks such as regularization and data augmentation
  • Learn to apply deep transfer learning to train an accurate model

Registration (Registration link is for Application Submission)

Industry professionals are encouraged to apply. There will be options to obtain continuing education units (CEUs) and professional development hours (PDH) credits. Participants are expected to have some familiarity with the Python language (we will share pre-workshop tutorials). Selected applicants will be notified on November 30, 2022. The registration cost for selected applicants is $25. There are 50 seats in total. Apply: https://bit.ly/apply2022dl

Accepted candidates will be sent specific registration or follow-up information.


  Date and Time

  Location

  Hosts

  Registration


  • Date: 16 Dec 2022
  • Time: 09:00 AM to 05:00 PM
  • All times are (UTC-06:00) Central Time (US & Canada)
  • Add_To_Calendar_icon Add Event to Calendar
  • University of Missouri-St. Louis (UMSL)
  • Saint Louis, Missouri
  • United States
  • Contact Event Host

  • If contact with host cannot be established, feel free to email at kaur.amardeep@ieee.org


  Speakers

Dr. Badri Adhikari

Topic:

Hand-on Workshop on Deep Learning

Biography:

Dr. Adhikari is an assistant professor of computer science at the University of Missouri-St. Louis (UMSL). His current research interests are: applied deep learning, interpretable artificial intelligence, and health/bio informatics. He is interested in investigating and develop deep learning methods, explainable/interpretable when possible, for improving human health.

His research is funded by: the University of Missouri-St. Louis (UMSL), NVIDIA, Google Cloud Credits, the U.S. National Science Foundation (NSF), and the National Aeronautics and Space Administration (NASA).

In 2021, he and two of his graduate students organized a full-day hands-on workshop on data visualization attended by around 30 participants. All the workshop materials and pictures are publicly accessible on his homepage at badriadhikari.github.io.




Agenda

Workshop Schedule

MORNING SESSION

  • Create deep learning models using automated tools

  • Mood recognition/
    classification using convolutional neural networks

  • Select your project/dataset and obtain initial accuracy

  • Regularize your model

LUNCH BREAK (Lunch will be provided)

AFTERNOON SESSION

  • Apply data augmentation and deep transfer learning

  • Introduce interpretable deep learning and ethics