Variational Autoencoders (VAEs) are generative deep learning models used to learn efficient representations of data and generate new, similar samples. VAEs combine neural networks with probabilistic methods to encode input data into a compressed latent space and then reconstruct it back. This training explains how encoders and decoders work together to model data distributions and generate realistic outputs. It also covers key concepts such as latent variables, loss functions, and reconstruction error. You will learn how VAEs are applied in image generation, anomaly detection, and data compression tasks. The course also highlights best practices for training stable models and improving generative quality in deep learning systems.
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