Machine Learning and Deep Learning
Autoencoders use an encoder and decoder. The encoder creates a latent space that the decoder then uses to generate the original image. There are various types of autoencoder neural networks like Convolutional Autoencoder, Variational Autoencoder, Denoising Autoencoder, etc.
This article explains how to use a deep learning neural network model to generate fictional celebrity faces. In particular, we use a convolutional variational autoencoder model to generate and train it on a real life celebrity images dataset. Then we try to generate fictional celebrity faces using this deep learning model. ...
This article explains the practical step s to build and train a deep learning convolutional variational autoencoder neural network using the PyTorch deep learning framework. We will be using the MNIST dataset for training the deep learning model. ...
This tutorial teaches you how to create a convolutional autoencoder in PyTorch to generate images of faces. ...
This blog post covers Variational Autoencoder using the PyTorch deep learning framework and the MNIST digit dataset, ...
In this tutorial, we will learn about sparse autoencoder neural networks using KL divergence. We will also implement sparse autoencoder neural networks using KL divergence with the PyTorch deep learning library. In the last tutorial, Sparse Autoencoders using L1 Regularization with PyTorch, we discussed sparse autoencoders using L1 regularization. We also learned how to code […] ...
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