To address these challenges, in this paper, we propose an end-to-end learning framework called deep ensemble machine (DEM) for video classification. Specifically, to establish effective ...

RMDL solves the problem of finding the best deep learning structure and architecture while simultaneously improving robustness and accuracy through ensembles of deep learning architectures. RDML can ...

Training deep learning models without using GPUs can be the difference between waiting a few minutes to waiting hours. Automated Text Classification In order to build predictive models, we need ...

Achieving collaboration in deep learning by using institutional incremental learning to address data sharing and security issues. Applicable to object detection problems for various domains. BT ...

A new computing architecture enables advanced machine-learning computations to be performed on a low-power, memory-constrained edge device. The technique may enable self-driving cars to make ...

In this article, we will implement the multiclass image classification using the VGG-19 Deep Convolutional Network used as a Transfer Learning framework where the VGGNet comes pre-trained on the ...

The use of deep learning has grown rapidly over the past decade, thanks to the adoption of cloud-based technology and use of deep learning systems in big data, according to Emergen Research, which ...

Discover how to build an automated intent classification model by leveraging pre-training data using a BERT encoder, BigQuery, and Google Data Studio.