This three-day workshop delves into advanced concepts of generative AI, focusing on GANs and Variational Autoencoders (VAEs), stable training techniques, and applications in creative arts, medicine, and bioinformatics. Participants will engage in hands-on sessions and case studies to understand practical challenges and solutions.

Aim

To provide PhD scholars and academicians with advanced skills in generative AI and Generative Adversarial Networks (GANs). This course aims to deepen understanding of GAN architectures, training techniques, and innovative applications, enhancing research and practical implementation capabilities.

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Workshop Objectives

Master advanced concepts of GANs and VAEs.
Implement stable GAN training techniques.
Develop innovative GAN applications in various fields.
Solve practical challenges in GAN training and optimization.
Enhance research and practical implementation skills.

Workshop Structure

Day 1: Advanced Generative AI Concepts

Lecture Topics:
- Detailed study of GANs: DCGAN, CycleGAN
- Variational Autoencoders (VAEs) and their applications
Discussion & Case Studies:
- Case studies in data augmentation and synthetic data generation
- Hands-on session with GAN architectures

Day 2: Training and Optimizing GANs

Lecture Topics:
- Techniques for stable GAN training: Wasserstein GAN, Progressive GAN
- Addressing common issues: mode collapse, convergence
Discussion & Case Studies:
- Practical challenges and solutions
- Interactive session with custom GAN projects

Day 3: Innovative Applications of GANs

Lecture Topics:
- Applications in creative arts: AI-generated art, music
- Use cases in medicine and bioinformatics
Discussion & Case Studies:
- Real-world impact and future trends
- Hands-on session with creative GAN projects

Participant’s Eligibility

Data scientists, AI researchers, computer vision engineers, and academicians in AI and machine learning.

Important Dates

Registration Ends

2024-10-23
Indian Standard Timing 1:00 pm

Workshop Dates

2024-10-23 to 2024-10-25
Indian Standard Timing 5 PM

Workshop Outcomes

Develop and optimize advanced GAN architectures.
Implement and fine-tune stable training techniques for GANs.
Create innovative applications of GANs in creative arts and healthcare.
Address and solve practical challenges in GAN training.
Conduct high-level research in generative AI and GANs.

Mentor Profile

Name: Karar Haider
Designation: AI – Engg
Affiliation: ML1

MR. SYED KRAR HAIDER BUKHARI is Lecturer in the Department of Computer Science and associated with Faculty of Computer Science and Information Technology, Superior University, Lahore. He received the Master of Science degree in computer science (MSCS) from FAST NUCES Islamabad, Pakistan in 2022. He has 5+ years of experience in Artificial Intelligence, machine learning, image processing, computer Vision, deep Learning ,NLP and Generative AI.He is currently working as an Data Scientist at digital DirAction. He is mentor and AI / ML Trainer at DirAction. He worked remotely as an AI / ML engineer at TPL UK based company. He is Editorial Board Member at STM Journals. He also worked remotely as an AI / ML engineer. He recived distinguished awards in research funds for his publications.His research publication includes in the areas of artificial intelligence, machine learning, image processing, computer Vision, NLP, and deep learning.

Fee Structure

Student

INR. 1499
USD. 40

Ph.D. Scholar / Researcher

INR. 1999
USD. 45

Academician / Faculty

INR. 2999
USD. 50

Industry Professional

INR. 4999
USD. 75

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