Innovative Topologies and Algorithms for Neural Networks
The introduction of new topologies and training procedures to deep neural networks has solicited a renewed interest in the field of neural computation. The use of deep structures has significantly improved state-of-the-art applications in many fields, such as computer vision, speech and text process...
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| Format: | Book Chapter |
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Basel, Switzerland
MDPI - Multidisciplinary Digital Publishing Institute
2021
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| Online Access: | Get Fullteks DOAB: description of the publication |
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| LEADER | 04105naaaa2200949uu 4500 | ||
|---|---|---|---|
| 001 | doab_20_500_12854_68541 | ||
| 005 | 20210501 | ||
| 020 | |a books978-3-0365-0285-4 | ||
| 020 | |a 9783036502847 | ||
| 020 | |a 9783036502854 | ||
| 024 | 7 | |a 10.3390/books978-3-0365-0285-4 |c doi | |
| 041 | 0 | |a English | |
| 042 | |a dc | ||
| 072 | 7 | |a KNTX |2 bicssc | |
| 100 | 1 | |a Xibilia, Maria Gabriella |4 edt | |
| 700 | 1 | |a Graziani, Salvatore |4 edt | |
| 700 | 1 | |a Xibilia, Maria Gabriella |4 oth | |
| 700 | 1 | |a Graziani, Salvatore |4 oth | |
| 245 | 1 | 0 | |a Innovative Topologies and Algorithms for Neural Networks |
| 260 | |a Basel, Switzerland |b MDPI - Multidisciplinary Digital Publishing Institute |c 2021 | ||
| 300 | |a 1 electronic resource (198 p.) | ||
| 506 | 0 | |a Open Access |2 star |f Unrestricted online access | |
| 520 | |a The introduction of new topologies and training procedures to deep neural networks has solicited a renewed interest in the field of neural computation. The use of deep structures has significantly improved state-of-the-art applications in many fields, such as computer vision, speech and text processing, medical applications, and IoT (Internet of Things). The probability of a successful outcome from a neural network is linked to selection of an appropriate network architecture and training algorithm. Accordingly, much of the recent research on neural networks has been devoted to the study and proposal of novel architectures, including solutions tailored to specific problems. This book gives significant contributions to the above-mentioned fields by merging theoretical aspects and relevant applications. | ||
| 540 | |a Creative Commons |f https://creativecommons.org/licenses/by/4.0/ |2 cc |4 https://creativecommons.org/licenses/by/4.0/ | ||
| 546 | |a English | ||
| 650 | 7 | |a Information technology industries |2 bicssc | |
| 653 | |a facial image analysis | ||
| 653 | |a facial nerve paralysis | ||
| 653 | |a deep convolutional neural networks | ||
| 653 | |a image classification | ||
| 653 | |a Chinese text classification | ||
| 653 | |a long short-term memory | ||
| 653 | |a convolutional neural network | ||
| 653 | |a Arabic named entity recognition | ||
| 653 | |a bidirectional recurrent neural network | ||
| 653 | |a GRU | ||
| 653 | |a LSTM | ||
| 653 | |a natural language processing | ||
| 653 | |a word embedding | ||
| 653 | |a CNN | ||
| 653 | |a object detection network | ||
| 653 | |a attention mechanism | ||
| 653 | |a feature fusion | ||
| 653 | |a LSTM-CRF model | ||
| 653 | |a elements recognition | ||
| 653 | |a linguistic features | ||
| 653 | |a POS syntactic rules | ||
| 653 | |a action recognition | ||
| 653 | |a fused features | ||
| 653 | |a 3D convolution neural network | ||
| 653 | |a motion map | ||
| 653 | |a long short-term-memory | ||
| 653 | |a tooth-marked tongue | ||
| 653 | |a gradient-weighted class activation maps | ||
| 653 | |a ship identification | ||
| 653 | |a fully convolutional network | ||
| 653 | |a embedded deep learning | ||
| 653 | |a scalability | ||
| 653 | |a gesture recognition | ||
| 653 | |a human computer interaction | ||
| 653 | |a alternative fusion neural network | ||
| 653 | |a deep learning | ||
| 653 | |a sentiment attention mechanism | ||
| 653 | |a bidirectional gated recurrent unit | ||
| 653 | |a Internet of Things | ||
| 653 | |a convolutional neural networks | ||
| 653 | |a graph partitioning | ||
| 653 | |a distributed systems | ||
| 653 | |a resource-efficient inference | ||
| 653 | |a pedestrian attribute recognition | ||
| 653 | |a graph convolutional network | ||
| 653 | |a multi-label learning | ||
| 653 | |a autoencoders | ||
| 653 | |a long-short-term memory networks | ||
| 653 | |a convolution neural Networks | ||
| 653 | |a object recognition | ||
| 653 | |a sentiment analysis | ||
| 653 | |a text recognition | ||
| 653 | |a IoT (Internet of Thing) systems | ||
| 653 | |a medical applications | ||
| 856 | 4 | 0 | |a www.oapen.org |u https://mdpi.com/books/pdfview/book/3562 |7 0 |z Get Fullteks |
| 856 | 4 | 0 | |a www.oapen.org |u https://directory.doabooks.org/handle/20.500.12854/68541 |7 0 |z DOAB: description of the publication |