Various Deep Learning Algorithms in Computational Intelligence

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This reprint highlights the importance of Deep Learning (DL), which has garnered significant attention in science, industry, and academia. It draws inspiration from the functioning of the human brain and the concept of learning. Unlike traditional and machine learning methods, deep learning techniques emulate the human brain's neural networks at a lower scale, allowing them to process and analyze substantial quantities of unstructured data. The remarkable proficiency of deep learning in unveiling intricate structures within extensive datasets genuinely resembles the extraordinary aptitude of the brain to recognize patterns and form complex connections. This unique characteristic allows DL to excel in modeling and solving complex problems across various scientific and technological fields. Just as the brain learns from experience, DL architectures learn through algorithms from data by adjusting numerous parameters during training to optimize their performance and accuracy. This concept of learning and adaptation is fundamental to DL's success. This reprint serves as an excellent opportunity to disseminate current knowledge beyond academic boundaries, reaching a diverse audience encompassing academics, professionals, and the general public. This wide readership fosters the potential for meaningful connections to established projects and the cultivation of collaboration for future research endeavors.

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Informasi Detail

Penerbit

Basel : MDPI - Multidisciplinary Digital Publishing Institute., 2023

Bahasa

English

ISBN/ISSN

978-3-0365-8139-2

Klasifikasi

-

Edisi

-

Subjek

machine learning
Deep Learning
n/a
PSO
sign language recognition
optimization of convolutional neural networks
breast segmentation
mammogram
semantic segmentation
radar image prediction
rain radar
precipitation nowcasting
UNet
PredRNN_v2
cubical complex
cubical homology
image classification
persistent homology
labor exports
Northeast Asian Countries
backpropagation neural network (BPNN)
k-Nearest Neighbor (kNN)
random forest regression (RFR)
decision making
emotion distribution learning
text-based emotion analysis
affective words
multi-task CNN
ghettos
GIS
networks
Washington D.C.
segregation
hybrid deep learning
time series
LSTM
Stacked LSTM
CNN-LSTMs
BDLSTM
CNN
GRU
modeling
SARS-CoV-2
generative adversarial network
image generation
image synthesis
GAN
generative model
Inception score
scoreGAN
waste classification
angle detection box
dynamic smoothing
YOLOv5
computer vision
object detection
traffic sign detection
you only look once (YOLO)
attention mechanism
feature fusion
barrier options
Black?Scholes model
polynomial regression
random forest regression
artificial neural network
option Greeks
data analysis
ski goggles lenses
surface defect
automatic optical inspection
Faster-RCNN
fine-tune
MobileNetV3
FPN
RPN
DNS
firewall
malicious
domain
name

File Digital

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