Название: Advanced Machine Learning Technologies and Applications. Proceedings of AMLTA 2020 Автор: Aboul Ella Hassanien, Roheet Bhatnagar
Издательство: Springer
Год: 2020 (2021 Edition)
Страниц: 737
Язык: английский
Формат: pdf (true)
Размер: 25.6 MB
This book presents the refereed proceedings of the 5th International Conference on Advanced Machine Learning Technologies and Applications (AMLTA 2020), held at Manipal University Jaipur, India, on February 13 - 15, 2019, and organized in collaboration with the Scientific Research Group in Egypt (SRGE). The papers cover current research in Machine Learning (ML), Deep Learning (DL), Big Data, text visualization, modeling optimization complex network, Internet of Things (IoT), biomedical engineering, fuzzy logic and security, as well as intelligence swarms and optimization.
Deep Learning is a class of Machine Learning which performs much bettero n unstructured data. Deep Learning techniques are outperforming current Machine Learning techniques. It enables computational models to learn features progressively from data at multiple levels. The popularity of deep learning amplified as the amount of data available increased as well as the advancement of hardware that provides powerful computers.
Deep learning techniques which implement deep neural networks became popular due to the increase of high-performance computing facility. Deep learning achieves higher power and flexibility due to its ability to process a large number of features when it deals with unstructured data. Deep learning algorithm passes the data through several layers; each layer is capable of extracting features progressively and passes it to the next layer. Deep neural networks are successful in supervised learning, unsupervised learning, reinforcement learning, as well as hybrid learning.
Deep learning architectures perform better than simple ANN, even though training time of deep structures is higher than ANN. However, training time can be reduced using methods such as transfer learning and GPU computing. One of the factors which decide the success of neural networks lies in the careful design of network architecture.
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