Variational Autoencoders
After being employed to achieve cutting-edge outcomes during image recognition and
reinforcement training, variational autoencoders (or VAEs) became popular. They've swiftly risen
to prominence as one of the most popular unsupervised learning approaches for complex
circumstances. Because VAEs employ neural networks that can be taught to identify stochastic
gradient descent, they are quite popular (smoothness properties). VAEs have showed
considerable potential in producing a wide range of complicated data models, such as models for
faces, handwritten numbers, and physical scene models.
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