Deep Learning Final Project: Built and trained Stacked CNN AutoEncoder and Deep CNN AutoEncoder based on STL-10 dataset - GitHub ... the process of convolution in keras using Theano. See details in ...

Deep learning project for human activity recognition using time-series data from smartwatches and VICON motion capture systems. Includes 1D-CNN, LSTM, and autoencoder pretraining, with improved models ...

Deep learning has been applied in physical-layer communications systems in recent years and has demonstrated fascinating results that were comparable or even better than human expert systems. In this ...

This study uses a hybrid deep learning technique to classify asphalt, pavement, and unpaved roads. In real-world circumstances, image data noise can damage image categorization algorithms. This issue ...

CNN is a classifier in machine learning and is an algorithm used to identify patterns of data such as images and videos using neural networks. The basic components for understanding CNN are as ...

MicroCloud Hologram Inc. (NASDAQ: HOLO), ("HOLO" or the "Company"), a technology service provider, they Announced the deep optimization of stacked sparse autoencoders through the DeepSeek open ...

In the work of Zhang et al. (2017), a deep learning framework consisting of the sparse autoencoder (SAE) and logistic regression was used to classify EEG emotion status. The sparse autoencoder was ...