Elevate your expertise in Generative AI with EdAthena's Post Graduate Program Specialization in Generative AI (PGPs - GenAI). This cutting-edge program equips professionals with the advanced knowledge and practical skills needed to harness the power of generative models and drive innovation in various domains.
The Post Graduate Program in AI and Generative AI (PGPS1) at EdAthena is designed to equip you with the essential skills required to excel in the field of artificial intelligence and generative models. Through comprehensive modules, hands-on projects, and real-world case studies, you will gain a deep understanding of generative AI techniques, including Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and autoregressive models. Each phase of the program builds on the previous one, ensuring a seamless learning experience and a robust foundation in AI and Generative AI. The curriculum covers essential topics such as image synthesis, style transfer, audio and text generation, scalable architectures, and ethical considerations in AI. By the end of the program, you will be well-equipped to develop, deploy, and manage advanced generative models, and apply your knowledge to create innovative AI solutions in various domains.
Comprehensive Curriculum: The program covers essential topics in Generative AI, including VAEs, GANs, and autoregressive models, ensuring a robust foundation in AI and Generative AI techniques.
Hands-On Learning: Emphasizes practical learning through real-world case studies and hands-on projects, allowing participants to apply theoretical knowledge to practical scenarios.
Specializations and Electives: Offers a range of specializations and electives, enabling participants to tailor their education to their specific interests and career goals, focusing on the latest trends and innovations in Generative AI.
A. Introduction to Generative Models
• Overview of Generative AI
• Types of Generative Models
B. Variational Autoencoders (VAEs) and Normalizing Flows
• VAE Architecture and Training
• VAE Variants and Extensions
C. Generative Adversarial Networks (GANs) and Autoregressive Models
• GAN Architecture and Training
• GAN Variants and Stability Techniques
A. Image Synthesis and Style Transfer
• StyleGANs and Progressive Growing
• Image-to-Image Translation (Pix2Pix, CycleGAN)
B. Audio and Text Generation
• Music, Speech, Text with GANs and VAEs
• Controllable Text Generation and Summarization
A. Deploying Generative Models
• Scalable Architectures for Generative Models
• Optimizing Generative Models for Production
B. Generative AI Ethics and Responsible AI
• Ethical Considerations in Generative AI
• Intellectual Property Rights and Generative Models
C. Capstone Project and Emerging Trends
• Implementing a Generative AI Solution
• Evaluating and Presenting Project Outcomes
