Understanding Variational Autoencoder | VAE Explained
In this video I deep dive into Variational Autoencoder (VAE) . If you're interested in understanding the inner workings of Variational Autoencoders, and how it differs from traditional autoencoder, you're in the right place.
🔍 In this video, we'll cover the following key points:
What is a Variational Autoencoder (VAE) and how does it work?
Difference between Autoencoder and Variational Autoencoder.
The loss function used in Variational Autoencoder to optimize their training.
Building your very own Variational Autoencoder
Conditional VAE (Conditional Variational Autoencoder)
⏱️ Timestamps
00:15 Video Highlights
00:32 Autoencoders
01:49 Need for Variational Auto Encoder
02:49 Transitioning to VAE from AutoEncoder
05:21 Modelling Data Generation in Variational AutoEncoder
06:18 Deriving Objective and Loss of VAE
08:24 Summary of Variational AutoEncoder Architecture
08:58 Conditional VAE
Resources Used in Making Video
1. Understanding Variational Autoencoders (VAEs) - https://tinyurl.com/vae-link1
2. Ali Ghodsi, Lec : Deep Learning, Variational Autoencoder, Oct 12 2017 [Lect 6.2] - • Video
3. Variational Autoencoders (VAEs): Generative AI I - https://tinyurl.com/vae-link2
Helpful Links
KL Divergence
1. Wikipedia - https://tinyurl.com/vae-link3
2. https://tinyurl.com/vae-link4
Computing P(x)
1. Sec 2.1 - https://tinyurl.com/vae-arxiv-link
2. https://tinyurl.com/vae-stack-1
3. https://tinyurl.com/vae-stack-2
Background Track - Fruits of Life by Jimena Contreras
Email : explainingai.official@gmail.com
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