- November 25, 2021
- 10:00 AM - 2:00 PM
Deep Learning - Module 1
Nov, 25th 2021
10:00 AM – 2:00 PM
Deep Learning – Module 1
DEEP LEARNING OVERVIEW AND ADDRESSING CHALLENGES AROUND DATA LABELLING
This workshop is in association with:
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?
In this workshop , we will talk about different kinds of neural networks architecture and their respective applications. Also, we would be discussing about several challenges in the deep learning workflow around data, network architecture, hyperparameter tuning and deployment. In detail we will introduce and talk about labeler apps and how can they help mitigate challenges around data labeling for AI applications.
Program Objectives
- Demonstrate a workflow for how you can research, develop and deploy your own deep learning application.
- Show where deep learning is being applied in engineering and science.
- Iteratively building and incorporating AI models for automating the annotations.
- Using and extending the labeler apps for Image/signal labelling.
- Preprocessing to facilitate image labelling.
Learning Outcomes
- Gain knowledge and insight about artificial intelligence and Deep learning as a technology.
- Learn about MATLAB’s end-to-end workflow from development to deployment for AI applications
- Capability to use MATLAB for addressing AI challenges around
- Data Labelling
- Hyperparameter tuning
- Embedded deployment
Workshop Schedule
To get a hands-on appreciation for Deep Learning with MATLAB, register to attend the free 2-hour Virtual Lab. Learn to solve complex problems related to images, with Deep Learning on MATLAB.
Guided by a team of professional engineers, you will write code using MATLAB Online to:
- Train deep neural networks on GPUs in the cloud.
- Create Deep Learning models from scratch for image.
- Explore pretrained models and use transfer learning.
- Import and export models from Python frameworks such as Keras and PyTorch.
- Automatically generate code for embedded targets.
No installation of MATLAB is necessary. Please make sure you have a strong internet connection to ensure optimal experience.
Who should attend?
Anybody who is
- working on artificial intelligence/deep learning applications
- looking forward to exploring artificial intelligence and deep learning as a domain
- interested to learn about Deep learning and Artificial intelligence using MATLAB
About the Speaker
Dr. Rishu Gupta
Senior application Engineer, MathWorks India.
Dr. 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 a focus on biomedical image processing for ultrasound images.