Kapak Resmi

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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Materyal Türü: Online
Dil:İngilizce
Baskı/Yayın Bilgisi: MDPI - Multidisciplinary Digital Publishing Institute 2021
Konular:
facial image analysis
facial nerve paralysis
deep convolutional neural networks
image classification
Chinese text classification
long short-term memory
convolutional neural network
Arabic named entity recognition
bidirectional recurrent neural network
GRU
LSTM
natural language processing
word embedding
CNN
object detection network
attention mechanism
feature fusion
LSTM-CRF model
elements recognition
linguistic features
POS syntactic rules
action recognition
fused features
3D convolution neural network
motion map
long short-term-memory
tooth-marked tongue
gradient-weighted class activation maps
ship identification
fully convolutional network
embedded deep learning
scalability
gesture recognition
human computer interaction
alternative fusion neural network
deep learning
sentiment attention mechanism
bidirectional gated recurrent unit
Internet of Things
convolutional neural networks
graph partitioning
distributed systems
resource-efficient inference
pedestrian attribute recognition
graph convolutional network
multi-label learning
autoencoders
long-short-term memory networks
convolution neural Networks
object recognition
sentiment analysis
text recognition
IoT (Internet of Thing) systems
medical applications
thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries
Online Erişim:ONIX_20210501_9783036502847_287
Etiketler: Etiketle
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Özet: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.

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