The traditional recommendation algorithm has existed the matter of cold start and data sparsity. To alleviate such problems, we propose a deep autoencoder algorithm for collaborative filtering ...

This project contains an Autoencoder, built and trained using Tensorflow, and used to vectorize images, so a kNN algorithm can check for image similarity. It contains two major Python notebooks, one ...

The rest of this manuscript is organized as follows. In Section 2, the patient data and algorithm were explained. In Section 3, the performance of the autoencoder was analyzed and compared with other ...

K. Raymond Choo, E. Bucheli-Susarrey, "Towards an interpretable autoencoder: A decision tree-based autoencoder and its application in anomaly detection," IEEE Transactions on Dependable and Secure ...

We present a novel granular computing approach that assesses landslide risk by combining fuzzy information granulation and a stacked autoencoder algorithm. The stacked autoencoder is trained using an ...

The demo sets up training parameters for the batch size (10), number of epochs to train (100), loss function (mean squared error), optimization algorithm (stochastic gradient descent) and learning ...