Скачать или смотреть pytorch vae tutorial

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Variational Autoencoders (VAEs) are a type of generative model that learn a probabilistic mapping between the input data and a latent space. VAEs are widely used for tasks like image generation, data compression, and representation learning. In this tutorial, we will implement a simple VAE using PyTorch.
Make sure you have the following libraries installed:
A VAE consists of two main parts: an encoder and a decoder. The encoder maps input data to a probability distribution in the latent space, and the decoder reconstructs the input data from samples drawn from the latent space. The loss function includes a reconstruction term and a regularization term to ensure that the latent space is well-behaved.
Let's implement a VAE for a simple dataset, such as MNIST. Create a new Python script and follow the steps below:
In this tutorial, we implemented a simple Variational Autoencoder using PyTorch for the MNIST dataset. You can extend this example to other datasets or modify the architecture for different tasks. VAEs are powerful generative models that find applications in various domains, including image generation and representation learning.
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