Variational Autoencoders (VAEs): Motivation, Math, ELBO & Generation of Data
After exploring the fundamentals of Latent Space in the previous video, it’s time to see how we actually generate new data from scratch. Our first stop is the Variational Autoencoder (VAE). In my opinion, this is the most intuitive way to bridge the gap from simple models to advanced deep learning in generative AI. In this video, we’ll dive deep into: The Intuition: Why standard Autoencoders fail at generation. The Math behind the VAE as the ELBO and the Reparameterization Trick. We will see how to sample from the latent space to create new outputs. And why VAEs struggle with high-fidelity images compared to newer architectures. Enjoy the video! Feel free to drop a comment with any questions—I’ll be happy to help. 00:00 – The Intuition Behind VAEs 01:27 – What are VAEs trying to achieve? 02:56 – How VAEs actually work 05:18 – The Math of VAEs & The ELBO 08:30 – Solving Backpropagation with the "Reparameterization Trick" 09:55 – Generating New Images with VAE! 10:32 – The "Blurriness Curse" 12:21 – Moving toward sharper generative models
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