Overview
In this session, we will take a deeper dive into designing, customizing, and training advanced neural networks. We will demonstrate MATLAB's extended deep learning framework, which enables you to implement advanced network architectures such as generative adversarial networks (GANs), variational autoencoders (VAEs), or Siamese networks.
Highlights
- Comparing basic and advanced network architectures to determine the right architecture for your needs
- Generating realistic synthetic image data with GANs
- Implementing 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.
