Masterclass on Variational Autoencoders: The First Step in Image Generation

Today, you can type "a cat dressed as an astronaut riding a bicycle on Mars", and in seconds, an AI generates that image — in stunning detail.
Tools like DALL·E, Stable Diffusion, and Midjourney feel like magic. But behind this modern marvel lies a fascinating journey, and it all began with one humble idea:
What if machines could learn to imagine? That’s where Variational Autoencoders (VAEs) come in. Before GANs and diffusion models took over, VAEs laid the groundwork for machines to learn compressed representations of data and use them to generate entirely new content.
In this beginner-friendly masterclass, we’ll explore the complete journey — from the basics of autoencoders to how VAEs work, what problem they solve, and why they matter.
What you will learn:

• Autoencoders 101: What they are, how they work, and how they compress data.
• Different types of autoencoders — and why we needed something more.
• The core idea behind Variational Autoencoders (VAEs)
• How VAEs help machines generate new, creative outputs
• How they connect to today’s tools like image generators and anomaly detectors
• Step-by-step breakdown of VAE’s architecture, training process, and real-world uses

Prerequisites:
Basic understanding of machine learning and neural networks is helpful, especially the idea of encoder-decoder architecture.
Whether you’re a student, AI enthusiast, or just curious about how machines learn to see and create, this session will help you understand one of the most important steps in the journey of generative AI.
Bonus: Attendees will receive FREE CERTIFICATE of participation.