<abstract xmlns="http://www.w3.org/1999/xhtml"><p>In recent years, many deep learning methods, allowed for a significant improvement of systems based on artificial intelligence methods. Their effectiveness results from an ability to analyze large labeled datasets. The price for such high accuracy is the long training time, necessary to process such large amounts of data. On the other hand, along with the increase in the number of collected data, the field of data stream analysis was developed. It enables to process data immediately, with no need to store them. In this work, we decided to take advantage of the benefits of data streaming in order to accelerate the training of deep neural networks. The work includes an analysis of two approaches to network learning, presented on the background of traditional stochastic and batch-based methods.</p></abstract>

In recent years, many deep learning methods, allowed for a significant improvement of systems based on artificial intelligence methods. Their effectiveness results from an ability to analyze large labeled datasets. The price for such high accuracy is the long training time, necessary to process such large amounts of data. On the other hand, along with the increase in the number of collected data, the field of data stream analysis was developed. It enables to process data immediately, with no need to store them. In this work, we decided to take advantage of the benefits of data streaming in order to accelerate the training of deep neural networks. The work includes an analysis of two approaches to network learning, presented on the background of traditional stochastic and batch-based methods.

On Training Deep Neural Networks Using a Streaming Approach

Journal of Artificial Intelligence and Soft Computing Research

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"On Training Deep Neural Networks Using a Streaming Approach"}

In recent years, many deep learning methods, allowed for a significant improvement of systems based on artificial intelligence methods. Their effectiveness...

On Training Deep Neural Networks Using a Streaming Approach

Pubblicato online: 11 dic 2019

Pagine: 15 - 26

Ricevuto: 12 set 2019

Accettato: 18 nov 2019

DOI: https://doi.org/10.2478/jaiscr-2020-0002

Parole chiave
deep learning, data streams, convolutional neural networks

© 2020 Piotr Duda et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

On Training Deep Neural Networks Using a Streaming Approach

Pubblicato online: 11 dic 2019

Pagine: 15 - 26

Ricevuto: 12 set 2019

Accettato: 18 nov 2019

DOI: https://doi.org/10.2478/jaiscr-2020-0002

Parole chiavedeep learning, data streams, convolutional neural networks

© 2020 Piotr Duda et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Parole chiave
deep learning, data streams, convolutional neural networks