Variational Autoencoders | Generative AI Animated

In this video you will learn everything about variational autoencoders. These generative models have been popular for more than a decade, and are still used in many applications. If you want to dive even deeper into this topic, I would suggest you read the original paper from Kingma, and an overview he wrote later on: - Auto-Encoding Variational Bayes https://arxiv.org/abs/1312.6114 - An Introduction to Variational Autoencoders https://arxiv.org/abs/1906.02691 If you want more accessible ressources, these blog posts by Matthew N. Bernstein are incredible to understand the different parts of the theory behind VAEs: - Variational Autoencoders https://mbernste.github.io/posts/vae/ - Variational Inference https://mbernste.github.io/posts/variational_inference/ - The Evidence Lower Bound https://mbernste.github.io/posts/elbo/ Chapters: 00:00 Introduction 01:05 Context 06:20 General Principle of VAEs 08:53 Evidence Lower Bound 11:01 The Reparameterization Trick 14:05 Training and Inference 16:28 Limitations 18:40 Bonus: ELBO derivations This video features animations created with Manim, inspired by Grant Sanderson's work at @3blue1brown. All the code for the animations of this video is available in the following github repository: https://github.com/ytdeepia/Variational-Autoencoders If you enjoyed the content, please like, comment, and subscribe to support the channel! #deeplearning #artificialintelligence #generativeai #machinelearning #manim #education #science