436 74 14MB
English Pages 412 Year 2022
本书版权归Arcler所有
本书版权归Arcler所有
Deep Learning Algorithms
本书版权归Arcler所有
本书版权归Arcler所有
Deep Learning Algorithms
Edited by: Zoran Gacovski
ARCLER
P
r
e
s
s
www.arclerpress.com
Deep Learning Algorithms Zoran Gacovski
Arcler Press 224 Shoreacres Road Burlington, ON L7L 2H2 Canada www.arclerpress.com Email: [email protected]
HERRN(GLWLRQ2 ISBN: (HERRN)
This book contains information obtained from highly regarded resources. Reprinted material sources are indicated. Copyright for individual articles remains with the authors as indicated and published under Creative Commons License. A Wide variety of references are listed. Reasonable efforts have been made to publish reliable data and views articulated in the chapters are those of the individual contributors, and not necessarily those of the editors or publishers. Editors or publishers are not responsible for the accuracy of the information in the published chapters or consequences of their use. The publisher assumes no responsibility for any damage or grievance to the persons or property arising out of the use of any materials, instructions, methods or thoughts in the book. The editors and the publisher have attempted to trace the copyright holders of all material reproduced in this publication and apologize to copyright holders if permission has not been obtained. If any copyright holder has not been acknowledged, please write to us so we may rectify. Notice: Registered trademark of products or corporate names are used only for explana © 2022 Arcler Press ISBN: 978-1-77469-183-0 (Hardcover) Arcler Press publishes wide variety of books and eBooks. For more information about Arcler Press and its products, visit our website at www.arclerpress.com
本书版权归Arcler所有
DECLARATION Some content or chapters in this book are open access copyright free published research work, which is published under Creative Commons License and are indicated with the citation. We are thankful to the publishers and authors of the content and chapters as without them this book wouldn’t have been possible.
本书版权归Arcler所有
本书版权归Arcler所有
ABOUT THE EDITOR
Dr. Zoran Gacovski has earned his PhD degree at Faculty of Electrical engineering, Skopje. His research interests include Intelligent systems and Software engineering, fuzzy systems, graphical models (Petri, Neural and Bayesian networks), and IT security. He has published over 50 journal and conference papers, and he has been reviewer of renowned Journals. Currently, he is a professor in Computer Engineering at European University, Skopje, Macedonia.
本书版权归Arcler所有
TABLE OF CONTENTS
List of Contributors .......................................................................................xv List of Abbreviations .................................................................................... xxi Preface.................................................................................................. ....xxiii Section 1: Methods and Approaches for Deep Learning Chapter 1
Advancements in Deep Learning Theory and Applications: Perspective in 2020 and Beyond ............................................................... 3 Abstract ..................................................................................................... 3 Introduction ............................................................................................... 4 Deep Network Topologies.......................................................................... 8 Application of Deep Learning .................................................................. 11 Modern Deep Learning Platforms ............................................................ 14 Training Algorithms.................................................................................. 17 Routine Challenges of Deep Learning ...................................................... 19 Available Open-Source Datasets .............................................................. 21 References ............................................................................................... 24
Chapter 2
Deep Ensemble Reinforcement Learning With Multiple Deep Deterministic Policy Gradient Algorithm....................................... 29 Abstract ................................................................................................... 29 Introduction ............................................................................................. 30 Background ............................................................................................. 32 Methods .................................................................................................. 34 Results and Discussion ............................................................................ 39 Conclusions ............................................................................................. 50 References ............................................................................................... 51
Chapter 3
Dynamic Decision-Making For Stabilized Deep Learning Software Platforms .................................................................................. 55 Abstract ................................................................................................... 55 Introduction ............................................................................................. 56 Stabilized Control for Reliable Deep Learning Platforms .......................... 57 The Use of Lyapunov Optimization for Deep Learning Platforms ............. 63 Emerging Applications ............................................................................. 68 Conclusions ............................................................................................. 69 Acknowledgements ................................................................................. 70 References ............................................................................................... 71
Chapter 4
Deep Learning For Hyperspectral Data Classification Through Exponential Momentum Deep Convolution Neural Networks ................ 73 Abstract ................................................................................................... 73 Introduction ............................................................................................. 74 Feature Learning ...................................................................................... 75 Structure Design of Hyperspectral Data Classification Framework ........... 76 Exponential Momentum Gradient Descent Algorithm .............................. 77 Experiment and Analysis .......................................................................... 80 Conclusion .............................................................................................. 86 Acknowledgments ................................................................................... 87 References ............................................................................................... 88
Chapter 5
Ensemble Network Architecture for Deep Reinforcement Learning .................................................................................................. 93 Abstract ................................................................................................... 93 Introduction ............................................................................................. 94 Related Work ........................................................................................... 95 Ensemble Methods for Deep Reinforcement Learning .............................. 97 Experiments ........................................................................................... 100 Conclusion ............................................................................................ 102 References ............................................................................................. 104
x
Section 2: Deep Learning Techniques Applied in Biology Chapter 6
Fish Detection Using Deep Learning ..................................................... 109 Abstract ................................................................................................. 109 Introduction ........................................................................................... 110 Literature Review ................................................................................... 111 Materials and Methods .......................................................................... 113 Data Augmentation ................................................................................ 118 Results and Discussion .......................................................................... 126 Conclusion ............................................................................................ 129 Acknowledgments ................................................................................. 130 References ............................................................................................. 131
Chapter 7
Can Deep Learning Identify Tomato Leaf Disease? ............................... 135 Abstract ................................................................................................. 135 Introduction ........................................................................................... 136 Related Work ......................................................................................... 137 Materials and Methods .......................................................................... 138 Experiments and Results ........................................................................ 143 Conclusion ............................................................................................ 149 Acknowledgments ................................................................................. 150 References ............................................................................................. 151
Chapter 8
Deep Learning For Plant Identification In Natural Environment ........... 157 Abstract ................................................................................................. 157 Introduction ........................................................................................... 158 Proposed Bjfu100 Dataset and Deep Learning Model ............................ 159 Experiments and Results ........................................................................ 162 Resnet26 on Flavia Dataset .................................................................... 165 Conclusion ............................................................................................ 166 Acknowledgments ................................................................................. 167 References ............................................................................................. 168
Chapter 9
Applying Deep Learning Models to Mouse Behavior Recognition ......... 171 Abstract ................................................................................................. 171 Introduction ........................................................................................... 172
xi
The Mouse Behavior Dataset ................................................................. 174 Experiments and Results ........................................................................ 175 Conclusions ........................................................................................... 186 Acknowledgements ............................................................................... 186 References ............................................................................................. 187 Section 3: Deep learning Applications in Medicine Chapter 10 Application of Deep Learning in Neuroradiology: Brain Hemorrhage Classification Using Transfer Learning ............................. 191 Abstract ................................................................................................. 191 Introduction ........................................................................................... 192 Related Work ......................................................................................... 194 Convolutional Neural Network .............................................................. 195 Transfer Learning ................................................................................... 196 Materials and Methods .......................................................................... 197 Results and Discussion .......................................................................... 204 Limitations ............................................................................................. 210 Conclusion ............................................................................................ 211 References ............................................................................................. 212 Chapter 11 A Review of the Application of Deep Learning in Brachytherapy.......... 217 Abstract ................................................................................................. 217 Introduction ........................................................................................... 218 Organ Delineation and Segmentation .................................................... 219 Segmentation and Reconstruction of the Applicator (Interstitial Needles) ..................................................................... 220 Dose Calculation ................................................................................... 222 Application of Treatment Planning System ............................................. 222 Others ................................................................................................... 223 Conclusions ........................................................................................... 224 References ............................................................................................. 225
xii
Chapter 12 Exploring Deep Learning and Transfer Learning for Colonic Polyp Classification ............................................................................... 229 Abstract ................................................................................................. 229 Introduction ........................................................................................... 230 Materials and Methods .......................................................................... 232 Results and Discussion .......................................................................... 242 Conclusion ............................................................................................ 250 Acknowledgments ................................................................................. 251 References ............................................................................................. 252 Chapter 13 Deep Learning Algorithm For Brain-Computer Interface ...................... 259 Abstract ................................................................................................. 259 Introduction ........................................................................................... 260 Critical Review of the Related Literature ................................................ 273 Comparison of Classification Algorithms ................................................ 276 Discussion ............................................................................................. 277 Methodology ......................................................................................... 280 Conclusion ............................................................................................ 281 References ............................................................................................. 282 Section 4: Deep Learning in Pattern Recognition Tasks Chapter 14 The Application of Deep Learning In Airport Visibility Forecast ........... 287 Abstract ................................................................................................. 287 Introduction ........................................................................................... 288 Deep Learning ....................................................................................... 288 The Establishment of Prediction Model .................................................. 289 Predictive Effect Test............................................................................... 291 Conclusions ........................................................................................... 295 References ............................................................................................. 297 Chapter 15 Hierarchical Representations Feature Deep Learning For Face Recognition............................................................................. 299 Abstract ................................................................................................. 299 Introduction ........................................................................................... 300 Images Preprocessing............................................................................. 302 Feature Extraction .................................................................................. 304 xiii
Designing the Classifiers of Supervised Learning .................................... 307 Designing the Classifier Combining Unsupervised and Supervised Learning .............................................................. 315 Experiments ........................................................................................... 322 Conclusion ............................................................................................ 332 Acknowledgements ............................................................................... 332 References ............................................................................................. 334 Chapter 16 Review of Research on Text Sentiment Analysis Based on Deep Learning .................................................................................. 341 Abstract ................................................................................................. 341 Introduction ........................................................................................... 342 Brief Review on the Research Progress of Text Sentiment Analysis .......... 343 Introduction to Text Sentiment Analysis Based on Deep Learning ........... 344 Summary and Prospect .......................................................................... 348 References ............................................................................................. 350 Chapter 17 Classifying Hand Written Digits With Deep Learning ........................... 353 Abstract ................................................................................................. 353 Introduction ........................................................................................... 354 Digit Classification with Deep Networks................................................ 354 Experiment ............................................................................................ 360 Conclusions ........................................................................................... 361 References ............................................................................................. 364 Chapter 18 Bitcoin Price Prediction Based on Deep Learning Methods ................................................................................................ 367 Abstract ................................................................................................. 367 Introduction ........................................................................................... 368 Dataset Exploration................................................................................ 368 Pre-Processing ....................................................................................... 369 Models .................................................................................................. 369 Results ................................................................................................... 371 Conclusion and Discussion.................................................................... 375 References ............................................................................................. 376 Index ..................................................................................................... 377
xiv
LIST OF CONTRIBUTORS Md Nazmus Saadat University of Kuala Lumpur, Malaysia Muhammad Shuaib University of Kuala Lumpur, Malaysia Junta Wu Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518071, China Huiyun Li Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518071, China Soohyun Park Korea University, Seoul, Republic of Korea Dohyun Kim Naver Webtoon Corporation, Seongnam, Republic of Korea Joongheon Kim Korea University, Seoul, Republic of Korea Qi Yue Xi’an Institute of Optics and Precision Mechanics, CAS, Xi’an 710119, China University of Chinese Academy of Sciences, Beijing 100039, China Xi’an University of Posts and Telecommunications, Xi’an 710121, China Caiwen Ma Xi’an Institute of Optics and Precision Mechanics, CAS, Xi’an 710119, China Xi-liang Chen Institute of Command Information System, PLA University of Science and Technology, No. 1, Hai Fu Road, Guang Hua Road, Qin Huai District, Nanjing City, Jiangsu Province 210007, China
Lei Cao Institute of Command Information System, PLA University of Science and Technology, No. 1, Hai Fu Road, Guang Hua Road, Qin Huai District, Nanjing City, Jiangsu Province 210007, China Chen-xi Li Institute of Command Information System, PLA University of Science and Technology, No. 1, Hai Fu Road, Guang Hua Road, Qin Huai District, Nanjing City, Jiangsu Province 210007, China Zhi-xiong Xu Institute of Command Information System, PLA University of Science and Technology, No. 1, Hai Fu Road, Guang Hua Road, Qin Huai District, Nanjing City, Jiangsu Province 210007, China Jun Lai Institute of Command Information System, PLA University of Science and Technology, No. 1, Hai Fu Road, Guang Hua Road, Qin Huai District, Nanjing City, Jiangsu Province 210007, China Suxia Cui Department of Electrical and Computer Engineering, Prairie View A&M University, Prairie View, TX 77446, USA Yu Zhou Department of Electrical and Computer Engineering, Prairie View A&M University, Prairie View, TX 77446, USA Yonghui Wang Department of Computer Science, Prairie View A&M University, Prairie View, TX 77446, USA Lujun Zhai Department of Electrical and Computer Engineering, Prairie View A&M University, Prairie View, TX 77446, USA Keke Zhang College of Engineering, Northeast Agricultural University, Harbin 150030, China Qiufeng Wu College of Science, Northeast Agricultural University, Harbin 150030, China Anwang Liu College of Engineering, Northeast Agricultural University, Harbin 150030, China xvi
Xiangyan Meng College of Science, Northeast Agricultural University, Harbin 150030, China Yu Sun School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China Yuan Liu School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China Guan Wang School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China Haiyan Zhang School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China Ngoc Giang Nguyen Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan; Dau Phan Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan; Favorisen Rosyking Lumbanraja Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan; Mohammad Reza Faisal Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan; Bahriddin Abapihi Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan; Bedy Purnama Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan;
xvii
Mera Kartika Delimayanti Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan; Kunti Robiatul Mahmudah Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan; Mamoru Kubo Institute of Science and Engineering, Kanazawa University, Kanazawa, Japan Kenji Satou Institute of Science and Engineering, Kanazawa University, Kanazawa, Japan Awwal Muhammad Dawud Department of Computer Engineering, Cyprus International University, Nicosia, Cyprus Kamil Yurtkan Department of Computer Engineering, Cyprus International University, Nicosia, Cyprus Huseyin Oztoprak Department of Computer Engineering, Cyprus International University, Nicosia, Cyprus Hai Hu Applied Nuclear Technology in Geosciences Key Laboratory of Sichuan Province, Chengdu University of Technology, Chengdu, China Yang Shao Applied Nuclear Technology in Geosciences Key Laboratory of Sichuan Province, Chengdu University of Technology, Chengdu, China Shijie Hu Applied Nuclear Technology in Geosciences Key Laboratory of Sichuan Province, Chengdu University of Technology, Chengdu, China Eduardo Ribeiro Department of Computer Sciences, University of Salzburg, Salzburg, Austria Department of Computer Sciences, Federal University of Tocantins, Palmas, TO, Brazil Andreas Uhl Department of Computer Sciences, University of Salzburg, Salzburg, Austria Georg Wimmer Department of Computer Sciences, University of Salzburg, Salzburg, Austria xviii
Michael Häfner St. Elisabeth Hospital, Vienna, Austria Asif Mansoor National University of Sciences and Technology, Islamabad, Pakistan Muhammad Waleed Usman National University of Computer and Emerging Sciences, Islamabad, Pakistan Noreen Jamil National University of Computer and Emerging Sciences, Islamabad, Pakistan M. Asif Naeem National University of Computer and Emerging Sciences, Islamabad, Pakistan Lei Zhu Training Center of Xinjiang Air Traffic Management Bureau, Urumqi, China Guodong Zhu College of Atmospheric Science, Nanjing University, Nanjing, China Meteorological Center of Xinjiang Air Traffic Management Bureau, Urumqi, China Lei Han Meteorological Center of Xinjiang Air Traffic Management Bureau, Urumqi, China Nan Wang Meteorological Center of Xinjiang Air Traffic Management Bureau, Urumqi, China Haijun Zhang Guangdong Provincial Key Laboratory of Conservation and Precision Utilization of Characteristic Agricultural Resources in Mountainous Areas, Meizhou, China School of Computing, Jiaying University, Meizhou, China Yinghui Chen Guangdong Provincial Key Laboratory of Conservation and Precision Utilization of Characteristic Agricultural Resources in Mountainous Areas, Meizhou, China School of Mathematics, Jiaying University, Meizhou, China Wenling Li College of Science, Yanbian University, Yanji, China Bo Jin College of Science, Yanbian University, Yanji, China
xix
Yu Quan Department of Economics and Management of Yanbian University, Yanji, China Ruzhang Yang Shanghai Foreign Language School, Shanghai, China Xiangxi Jiang Barstow School of Ningbo, Ningbo, China
xx
LIST OF ABBREVIATIONS
AUV
Autonomous underwater vehicle
BA
Boltzmann addition
BM
Boltzmann multiplication
CPU
Central processing unit
CAD
Computer-aided diagnosis
CT
Computer tomography
CV
Computer vision
CNN
Convolution neural network
DBN
Deep belief network
DCNN
Deep convolution neural network
DNNs
Deep neural network
DRL
Deep reinforcement learning
DRBM
Deep restricted Boltzmann machine
DPG
Deterministic policy gradient
GRU
Gated recurrent units
GPU
Graphical processing unit
HBPNNs
Hybrid BP neural networks
ICH
Intracranial haemorrhage
JELSR
Joint embedding learning and sparse regression
LR
Logistic regression
LSTM
Long short-term memory network
MV
Majority voting
MDP
Markov decision processes
MRSF
Minimum redundancy spectral feature selection
MVEP
Motion-onset visual evoked potential
MLP
Multi-layer perceptron
NIRS
Near-infrared spectroscopy
PMI
Pointwise mutual information
PFCL
Prior fully connected layers
QDA
Quadratic discriminant analysis
RBF
Radial Basis Function
RH
Relative humidity
RBM
Restricted Boltzmann machine
RMSE
Root mean square error
SVD
Singular value decomposition
SCP
Slow cortical potentials
TORCS
The open racing car simulator
xxii
PREFACE
The Deep learning is a branch of machine learning based on data presentation via complex representations with high degree of abstraction - that are obtained by applying learned nonlinear transformations. Deep learning methods find their application in important areas of artificial intelligence, such as: computer vision, natural language processing, speech and sound comprehension, as well as in bioinformatics. Deep learning is a class of machine learning algorithms that:
uses multilayer nonlinear processor units to extract and transform features. Each subsequent layer takes as input the output elements of the previous layer. learns in a supervised and / or unsupervised manner. learns a number of levels of representation - corresponding to different degrees of abstraction. uses some form of descending gradient algorithm to train through error backpropagation. The layers used in deep programming include the hidden layers of the artificial neural network and a multitude of statement formulas. This book covers the most important discriminant and generative deep models with special emphasis on practical implementations. We cover the key elements of classical neural networks and provides an overview of the building blocks, regularization techniques, and learning methods that are specific to deep models. Also we consider the deep convolutional models and illustrates their application in image classification and natural language processing. The generative deep models are often used in computer vision applications and natural language processing. Sequence modeling by deep feedback neural networks can be applied in the field of natural language processing. Practical implementations of deep learning are made in modern dynamic languages (Python, Lua or Julia), and also with application frameworks for deep learning (e.g. Theano, TensorFlow, Torch). This edition covers different topics from deep learning algorithms, including: methods and approaches for deep learning, deep learning applications in biology, deep learning applications in medicine, and deep learning applications in pattern recognition systems. Section 1 focuses on methods and approaches for deep learning, describing advancements in deep learning theory and applications - perspective in 2020 and beyond; deep ensemble reinforcement learning with multiple deep deterministic policy gradient algorithm; dynamic decision-making for stabilized deep learning software
platforms; deep learning for hyperspectral data classification through exponential momentum deep convolution neural networks; and ensemble network architecture for deep reinforcement learning. Section 2 focuses on deep learning applications in biology, describing fish detection using deep learning; deep learning identification of tomato leaf disease; deep learning for plant identification in natural environment; and applying deep learning models to mouse behavior recognition. Section 3 focuses on deep learning applications in medicine, describing application of deep learning in neuroradiology: brain hemorrhage classification using transfer learning; a review of the application of deep learning in brachytherapy; exploring deep learning and transfer learning for colonic polyp classification; and deep learning algorithm for brain-computer interface. Section 4 focuses on deep learning applications in pattern recognition systems, describing application of deep learning in airport visibility forecast; hierarchical representations feature deep learning for face recognition; review of research on text sentiment analysis based on deep learning; classifying hand written digits with deep learning; and bitcoin price prediction based on deep learning methods.
SECTION 1:
Methods and Approaches for Deep Learning
CHAPTER 1
ADVANCEMENTS IN DEEP LEARNING THEORY AND APPLICATIONS: PERSPECTIVE IN 2020 AND BEYOND Md Nazmus Saadat and Muhammad Shuaib University of Kuala Lumpur, Malaysia
ABSTRACT The aim of this chapter is to introduce newcomers to deep learning, deep learning platforms, algorithms, applications, and open-source datasets. This chapter will give you a broad overview of the term deep learning, in context to deep learning machine learning, and Artificial Intelligence (AI) is also introduced. In Introduction, there is a brief overview of the research achievements of deep learning. After Introduction, a brief history of deep learning has been also discussed. The history started from a famous scientist called Allen Turing (1951) to 2020. In the start of a chapter after
Citation: Md Nazmus Saadat and Muhammad Shuaib (December 9th 2020). Advancements in Deep Learning Theory and Applications: Perspective in 2020 and beyond, Advances and Applications in Deep Learning, Marco Antonio Aceves-Fernandez, IntechOpen, DOI: 10.5772/intechopen.92271. Copyright: © 2020 by authors and IntechOpen. This paper is an open access article distributed under a Creative Commons Attribution 3.0 License .
4
Deep Learning Algorithms
Introduction, there are some commonly used terminologies, which are used in deep learning. The main focus is on the most recent applications, the most commonly used algorithms, modern platforms, and relevant opensource databases or datasets available online. While discussing the most recent applications and platforms of deep learning, their scope in future is also discussed. Future research directions are discussed in applications and platforms. The natural language processing and auto-pilot vehicles were considered the state-of-the-art application, and these applications still need a good portion of further research. Any reader from undergraduate and postgraduate students, data scientist, and researchers would be benefitted from this. Keywords:- Deep learning, machine learning , neural networks
INTRODUCTION Deep learning is focusing comprehensively on video, image, text and audio recognition, autonomous driving, robotics, healthcare, etc. [1]. Deep learning is a result orientated field of study that why getting very much attention from researcher and academicians. The Rina Dechter introduced the word of deep learning in 1986, the main motivation behind the advent of field deep learning was making an intelligent machine that mimic the human brain. In humans, the brain is the most important and decision-making organ; brain takes decision based on sight, smell, touch, and sounds. The brain also can store memory and solve complex problems based on their experience. For the last few decades, the researchers dreamed of making a machine that is as intelligent as, like our brains, they started studying the biological structure and working of the human brain. Making a robot that performs certain duties and self-driving cars is to reduce roadside incidents. Because according to the World Health Organization (WHO), 1.35 million people die every year in road incidents [2] and approximately 90% of the incidents are due to human errors [3]. To develop state-of-the-art devices for the applications listed above, ones need to think in a different way of of the most innovative paradigms that make it possible up to some extent. In deep learning, the word deep indicates the number of layers through which ! " intelligence machine learning or deep learning because all these overlap
Advancements In Deep Learning Theory And Applications: ...
5
each other some way or the other. Machine learning is any sort of computer program that can learn by their own without having specially programmed by the programmer. There are two types of machine learning: supervised learning and unsupervised learning. In supervised learning, you teach or train the machine with a fully labeled data, the machine learns from the labeled data and then anticipate the unforeseen data. In supervised learning, the machine can only give you correct output when the input is already experienced in training phase; it is based on experience; the more is the training dataset or experience of your machine the higher is the chances of getting the actual output. It is a time-consuming process and also required a lot of expertise in data science. On the other hand, in unsupervised learning, supervision of a model is not needed, rather the model work on its own catches new data and discovers the information inside the data. It usually deals with label-less data; compared to supervised learning, unsupervised patterns. Deep learning models are agile and result oriented in terms of complicated abstractions. Deep learning models are mostly based on ANN, categorically CNNs, although there are deep belief networks, generative models, propositional formulas and Boltzmann machine also play their part (Figure 1).
Figure 1. Deep learning a subset of machine learning and AI.
Deep learning has been evaluated as a game-changer in AI and computer vision. Today, state-of-the-art object detection is possible only due to deep learning [4]; traditional methods of object detection are not enough to cater with detection so smartly. To understand the whole image of object detection, #
6
Deep Learning Algorithms
calculate the concept and locations of the objects in every image, that is, object detection which is based on face detection, pedestrian detection, and skeleton detection [5]. Deep learning has cutting-edge technology healthcare. It has a very deep impact on the life of the people or societies because its application is always the need of the day. The deep learning $ # data analytics. Big data analytics is the number of complicated processes ' * methods used to identify the hidden patterns, unknown correlations market trends, and customer preference from huge dataset. Big data analytics can # # # Deep learning is an emerging area of research and modern application. The deep learning + covers industry, business, and healthcare; it combines all the hot research