This project demonstrates the implementation of a Variational Autoencoder (VAE) using TensorFlow and Keras on the MNIST dataset. The VAE is a generative model that learns to encode input data into a ...

Abstract: Ensuring the reliability and safety of industrial processes requires robust monitoring systems capable of detecting and diagnosing issues in complex, dynamic, and high-dimensional data ...

This repo contains all my Deep Learning semester work, including implementations of FNNs, CNNs, autoencoders, CBOW, and transfer learning. I explored TensorFlow, Keras, PyTorch, and Theano while ...

Abstract: Unlike other deep learning (DL) models, Transformer has the ability to extract long-range dependency features from hyperspectral image (HSI) data. Masked autoencoder (MAE), which is based on ...