Deep Learning Series - Session 1: Deep Learning Overview

Overview

In the first part, David Kirschner will show how an LSTM network was used for signal processing to automatically pick the arrival times of seismic P- and S-waves. The network picks events accurately and quickly, obviating the need for human intervention. Although the deep learning model was trained to detect small, local earthquakes in an onshore fold-thrust belt, it is effective in picking small earthquakes in other geologic settings and large earthquakes recorded on global seismic networks based on some initial results.

The aim of the second part is to provide an overview of how MATLAB enables you to take advantage of disruptive technologies like deep learning.
Deep Learning is a key technology driving the current Artificial Intelligence (AI) megatrend. You may have heard of some mainstream applications of deep learning, but how many of them would you consider applying to your engineering and science applications? MathWorks developers have purpose-built MATLAB's deep learning functionality for engineering and science workflows. We understand that success goes beyond just developing a deep learning model. Ultimately, models need to be incorporated into an entire system design workflow to deliver a product or a service to the market.

Highlights

• Deep learning applications in engineering and science
• Workflow for researching, developing and deploying your deep learning application
• How to get started with deep learning in MATLAB

About the Presenters

David Kirschner is a research geologist with Royal Dutch Shell in Houston, Texas.  He has worked on numerous research projects over the past nine years with Shell including a large passive-source seismic project that necessitated development of a MATLAB-focused project in collaboration with MathWorks consultants.  He also spends time consulting for Shell business units on exploration and production projects in the Gulf of Mexico.  Prior to joining Shell in 2012, he was a professor at Saint Louis University for sixteen years.  He has a Ph.D. in Geology from the University of Minnesota and held a four-year post-doctoral research position in Geochemistry and Geology at the Universite de Lausanne, Switzerland.

Toon Weyens is an application engineer at MathWorks in Eindhoven, Netherlands. He supports innovative companies in automation and machinery, automotive, and aerospace industries by helping them use MathWorks software to analyze data, develop algorithms, create mathematical models, and scale to run on clusters, GPUs, and clouds. Prior to joining MathWorks, Toon was a postdoctoral researcher at the ITER Organization. He holds a M.Sc. degree in Energy Engineering from the University of Leuven, a M.Sc. degree in Nuclear Fusion Science and Technology from the Universidad Carlos III in Madrid, and a Ph.D. degree in Applied Physics from Eindhoven University of Technology.

Paola Jaramillo is an application engineer at MathWorks in Eindhoven, Netherlands. She supports customers in finding the right solutions for doing signal processing with MATLAB and Simulink in different application areas including image processing, computer vision, and machine learning. Her primary interests are sensor data analytics, embedded systems, and self-adaptive systems. Before joining MathWorks, she was awarded a fellowship under an international double degree agreement with the Politecnico di Torino in Italy, where she graduated in Electronic Engineering. She carried out a six-month internship at IBM Zurich Research Laboratories in Switzerland, working on the design and implementation of a DSP module for FPGAs. During her research experience at Eindhoven University of Technology, she focused on sensor data analytics for intelligent lighting environments and actively participated in several European Commission projects.