Introduction to Generative AI

Understanding Generative AI

Introduction to Generative AI

• What is Generative AI?
• Overview of generative models and their significance in AI.

Applications of Generative AI

• Real-world use cases and industries benefiting from generative models.

Generative Models

• Probabilistic Models
• Introduction to probability theory and its role in generative models.

Autoencoders

• Understanding autoencoders and their use in data compression.

Variational Autoencoders (VAEs)

• Introduction to VAEs and their applications in generating new data.

Generative Adversarial Networks (GANs)

• GAN Fundamentals
• Deep dive into the GAN architecture and how it works.

Training GANs

• The process of training GANs to generate data.

Hands-On Workshop: GANs

• Setting Up GANs
• Practical setup and environment for GAN development.

Building a Simple GAN

• Participants create a basic GAN model.

Conditional GANs and StyleGAN

• Conditional GANs
• Using GANs for conditional data generation.

StyleGAN

• Introduction to StyleGAN and its use in generating high-quality images.

Text Generation and Language Models

• Language Models
• Understanding language models and their generative capabilities.

GPT and Transformers

• Deep dive into GPT-3 and other transformer-based language models.

Hands-On Workshop: Text Generation

• Text Generation Setup
• Setting up the environment for text generation using GPT-3.

Generating Text with GPT-3

• Participants use GPT-3 for text generation.

Ethical Considerations and Future Trends

• Ethical Use of Generative AI
• Discussing the ethical responsibilities when working with generative models.

Future Trends

• Exploring the evolving landscape of generative AI and potential future applications.