IEEE Education Society Phoenix Chapter: MATLAB Workshop on Deep Learning

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Deep learning is quickly becoming embedded in everyday applications. It’s becoming essential for students and educators to adopt this technology to solve complex real-world problems. MATLAB and Simulink provide a flexible and powerful platform to develop and automate data analysis, deep learning, AI, and simulation workflows in a wide range of domains and industries. In this workshop we will introduce deep learning with MATLAB. We will utilize a previously trained network and modify it, using the MATLAB Deep Network Designer. The Deep Network Designer allows you to interactively build, visualize, and train neural networks. Individuals can generate the code for the neural network and fine- tune parameters. Users can use popular pre-trained networks or construct their own. This visual approach implemented by the Deep Network Designer creates a more efficient workflow.



  Date and Time

  Location

  Hosts

  Registration



  • Date: 03 Oct 2023
  • Time: 12:00 PM to 02:00 PM
  • All times are (UTC-07:00) Arizona
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  • Tempe, Arizona
  • United States 85281
  • Building: John W. Schwada (SCOB)
  • Room Number: 290
  • Click here for Map

  • Contact Event Host
  • asumathworks@gmail.com

  • Co-sponsored by MathWorks


  Speakers

Jon of MathWorks

Topic:

MATLAB Workshop on Deep Learning

Deep learning is quickly becoming embedded in everyday applications. It’s becoming essential for students and educators to adopt this technology to solve complex real-world problems. MATLAB and Simulink provide a flexible and powerful platform to develop and automate data analysis, deep learning, AI, and simulation workflows in a wide range of domains and industries. In this workshop we will introduce deep learning with MATLAB. We will utilize a previously trained network and modify it, using the MATLAB Deep Network Designer. The Deep Network Designer allows you to interactively build, visualize, and train neural networks. Individuals can generate the code for the neural network and fine- tune parameters. Users can use popular pre-trained networks or construct their own. This visual approach implemented by the Deep Network Designer creates a more efficient workflow.

Biography:

Jon Loftin is a Customer Success Engineer at MathWorks. Jon’s background is in mathematics. More specifically, implementing mathematics in a computer. He holds degrees in mathematics: a BS from Southern Arkansas University, a MS from the University of Arkansas, and a Ph.D. from Texas Tech University. He has had years of teaching experience, from teaching at the Naval Nuclear Power School to teaching as an Assistant Professor. Jon’s research focus is building efficient integration techniques in finite element methods.

Address:United States