Deep Learning Webinar Series

In this 4-part Deep Learning Webinar Series you will learn how easy it can be to apply Deep Learning in your engineering and science projects using MATLAB and Simulink. With just a few lines of MATLAB code you can apply deep learning techniques to your work, from standard architectures for image and signal processing to advanced neural networks for a wide range of tasks. Also learn how automation can help you label your data more efficiently, and how optimization techniques for hyperparameters can be used to maximize the performance of your networks.

Highlights

The series focuses on the following topics:

• Demonstrate a workflow for how you can research, develop, and deploy your deep learning application
• Use and extend the Signal, Image and Video Labelers to automate your data labeling workflow
• Graphically create, edit, and train models
• Efficiently explore and optimize model hyperparameters with interactive apps and optimization-based approaches
• Customize and train advanced neural networks
• Generate realistic synthetic image data with GANs
• Implement generalized research models in MATLAB

About the Presenter

Rishu Gupta is a senior application engineer at MathWorks India. He primarily focuses on image processing, computer vision, and deep learning applications. Rishu has over nine years of experience working on applications related to visual contents. He previously worked as a scientist at LG Soft India in the Research and Development unit. He has published and reviewed papers in multiple peer-reviewed conferences and journals. Rishu holds a bachelor’s degree in electronics and communication engineering from BIET Jhansi, a master’s in visual contents from Dongseo University, South Korea, working on the application of computer vision, and a Ph.D. in electrical engineering from University Technology Petronas, Malaysia with focus on biomedical image processing for ultrasound images.