Variational Autoencoders (VAEs) are probabilistic generative models that learn to encode data into a continuous latent space and reconstruct it through a decoder, introduced by Kingma and Welling in 2013. Unlike traditional autoencoders, VAEs impose a structured probabilistic distribution (typically Gaussian) on the latent space, enabling them to generate new, realistic samples by sampling from this learned distribution. The key insight is the reparameterization trick, which makes the stochastic sampling process differentiable, allowing end-to-end training via backpropagation. VAEs optimize the Evidence Lower Bound (ELBO), balancing reconstruction quality with regularization to prevent overfitting — a tension that makes them both powerful and nuanced to train effectively.