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 ...

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 ...

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 ...

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 ...

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 ...

This paper will focus on how to utilize Onto Innovation’s TrueADC software product to build ADC classifiers using a multi-engine (ME) solution. The software supports CNN, DNN and KNN algorithms. 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.