Understanding Variational Autoencoder | VAE Explained
About this lesson
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] - https://www.youtube.com/watch?v=uaaqyVS9-rM&ab_channel=DataScienceCourses 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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