Variational Autoencoder
A generative model that learns a probabilistic latent space, allowing sampling of new data points similar to training data.
Related Concepts
- Autoencoder: Explore how Autoencoder relates to Variational Autoencoder
- Generative Model: Explore how Generative Model relates to Variational Autoencoder
- Latent Space: Explore how Latent Space relates to Variational Autoencoder
- KL Divergence: Explore how KL Divergence relates to Variational Autoencoder
Why It Matters
Understanding Variational Autoencoder is crucial for anyone working with neural networks & deep learning. This concept helps build a foundation for more advanced topics in AI and machine learning.
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This term is part of the comprehensive AI/ML glossary. Explore related terms to deepen your understanding of this interconnected field.
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Related Terms
Autoencoder
An unsupervised neural network that learns to compress data into a latent representation and reconstruct it, useful for dimensionality reduction.
KL Divergence
Kullback-Leibler divergence - a measure of how one probability distribution differs from another.
Latent Space
A compressed, learned representation space where similar data points are close together, used in autoencoders and VAEs.