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020			_a9781394269266
020			_a9781394269297 _qelectronic book
020			_a1394269293 _qelectronic book
020			_a1394269277 _qelectronic book
020			_a9781394269280 _q(electronic bk.)
020			_a1394269285 _q(electronic bk.)
020			_a9781394269273 _q(electronic bk.)
020			_z9781394269266 _qhardcover
020			_z1394269269 _qhardcover
024	8		_aCIPO000228625
035		9	_a(GOBI)90104075606
035			_a(OCoLC)1514660050 _z(OCoLC)1514644872
037			_a11080448 _bIEEE
040			_aYDX _beng _erda _cYDX _dOCLCO _dOCLCQ _dUKMGB _dOCLCO _dOCLCQ _dYDX _dDG1 _dIEEEE _dCLOUD _dUKAHL _dHOPLA _dOCLCO _dN$T _dOCLCO _dOCLCL _dOCLCO
041			_aeng
050		4	_aQ325.73 _b.A75 2025
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072		7	_aCOM016000 _2bisacsh/2024
072		7	_aCOM000000 _2bisacsh/2024
072		7	_aTEC009070 _2bisacsh/2024
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072		7	_aTEC008000 _2bisacsh/2024
082	0	4	_a006.3/12 _223
100	1		_aArif, Tariq M., _eauthor.
245	1	0	_aDeep learning on embedded systems : _ba hands-on approach using Jetson Nano and Raspberry Pi / _cby Tariq M Arif.
264		1	_aHoboken, New Jersey : _bJohn Wiley & Sons, Inc., _c[2025]
300			_a1 online resource ( ix, 242 pages) : _billustrations
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
504			_aIncludes bibliographical references and index.
505	0		_aPreface x -- Acknowledgment xi -- Biography xii -- About the Companion Websitexiii -- 1 Introduction 1 -- 1.1 Machine Learning to Deep Learning 1 -- 1.2 Modern Embedded Systems 2 -- 1.3 Deep Transfer Learning in Embedded System 4 -- 1.4 Deep Learning Frameworks: An Overview 5 -- 1.4.1 PyTorch 5 -- 1.4.2 TensorFlow 6 -- 1.4.3 Other Major Frameworks 6 -- 1.5 Deep Learning and AI: Big Data and the Road Ahead 7 -- 1.5.1 Advances in Computational Hardware 7 -- 1.5.2 Adoptions in Industry and Business 8 -- 1.5.3 Ethical, Legal, and Privacy Concerns 8 -- 1.5.4 Future Challenges 9 -- 2 Fundamentals of Deep Learning 15 -- 2.1 Neural Networks Overview 15 -- 2.2 Basic Concepts and Terminologies 15 -- 2.2.1 Neurons and Layers 15 -- 2.2.2 Role of Activation Functions 17 -- 2.2.3 Learning Types 18 -- 2.3 Training on a Network 18 -- 2.3.1 Forward and Backward Propagation 18 -- 2.3.2 Loss Functions 19 -- 2.4 Gradient Descent Algorithm 19 -- 2.5 Weight Initialization and Regularization 21 -- 2.6 Hyperparameter Tuning 22 -- 2.6.1 Architecture-based Hyperparameters 23 -- 2.6.2 Training-based Hyperparameters 23 -- 2.7 Overview of Common Hyperparameters 23 -- 2.7.1 Kernel Size 23 -- 2.7.2 Learning Rate 23 -- 2.7.3 Weight Decay 24 -- 2.7.4 Dropout 24 -- 2.7.5 Batch, Iteration, and Epochs 24 -- 2.8 Challenges and Best Practices for Efficient Tuning 25 -- 3 Convolutional and Recurrent Neural Network 27 -- 3.1 Introduction 27 -- 3.2 Historical Background 27 -- 3.3 Convolutional Neural Networks 28 -- 3.3.1 CNN Framework 28 -- 3.3.2 Convolutional Operations 28 -- 3.3.3 Padding and Stride 30 -- 3.3.4 Pooling Layers 31 -- 3.3.5 Fully Connected Layers 32 -- 3.3.6 Output Layers 32 -- 3.3.7 Overall Architecture 32 -- 3.4 Recurrent Neural Networks 33 -- 3.4.1 RNN Framework 33 -- 3.4.2 Basic Architectures 33 -- 3.4.3 Backpropagation Through Time 34 -- 3.4.4 Long Short-term Memory Networks 35 -- 3.4.5 Gated Recurrent Units 35 -- 3.4.6 Bidirectional RNNs 35 -- 3.5 Applications 36 -- 3.6 Conclusion 37 -- 4 Deep Learning Using PyTorch 41 -- 4.1 Introduction to PyTorch 41 -- 4.2 Anaconda and PyTorch for Windows System 41 -- 4.2.1 Anaconda Installation and Environment Setup 42 -- 4.2.2 Setting up the PyTorch Framework 44 -- 4.2.2.1 Visual Studio Professional 47 -- 4.2.2.2 CUDA and cuDNN Installation 48 -- 4.3 Other Essential Packages for Deep Learning 50 -- 4.4 Introduction to Tensor 52 -- 4.4.1 Defining a Tensor 52 -- 4.4.2 Tensors in PyTorch 53 -- 4.5 Basic Torch Operations in PyTorch 53 -- 4.6 Gradient Calculation in PyTorch 55 -- 4.7 Exercise Problems 57 -- 5 Introduction to Jetson Nano and Setup 59 -- 5.1 Introduction to Jetson Embedded Devices 59 -- 5.1.1 Jetson Nano 60 -- 5.1.2 Hardware and Power Requirements 61 -- 5.2 Jetpack Installation 62 -- 5.3 Direct Setup 65 -- 5.3.1 Increase Root Partition Size 65 -- 5.3.2 Other Settings 68 -- 5.3.3 Wi-Fi Driver 70 -- 5.4 Configure Visual Studio Code on Jetson 71 -- 5.5 OpenCV and PyTorch in Jetson 75 -- 5.6 Setting up Jetson Inference 76 -- 5.7 OpenCV Library and Test Video Capture Functionality 79 -- 5.8 Conclusion 81 -- 5.9 Exercise Problems 81 -- 6 Linux Terminal Overview 85 -- 6.1 Introduction 85 -- 6.2 Basic Terminal Commands and Syntax 86 -- 6.3 Overview of File System 87 -- 6.4 Navigating Files and Directories 87 -- 6.5 Create, Edit, and Delete 90 -- 6.5.1 Copy and Move Operations 92 -- 6.6 Create and Execute Python Code from Terminal 93 -- 6.7 Common Wildcard Characters 95 -- 6.8 Find, View, and Get Information 96 -- 6.9 Permission and Ownership 99 -- 6.9.1 Permissions Using Octal and Symbolic Notations 100 -- 6.9.2 Update Permission Using Operators 101 -- 6.10 Install and Uninstall Packages Using "sudo" 104 -- 6.11 Conclusion 105 -- 7 Docker Engine Setup 107 -- 7.1 Introduction to Docker Engine 107 -- 7.2 Docker in Embedded Devices 107 -- 7.3 Jetson Inference Docker 108 -- 7.3.1 Download and Test Pre-trained Models 109 -- 7.4 Using Host Files in Docker Environment 110 -- 7.4.1 Create Executable Python 111 -- 7.4.2 Attach Host Directory to Root System and Testing 113 -- 7.5 Building a Docker Image 115 -- 7.5.1 Runtime Image for MediaPipe Ecosystem 115 -- 7.5.2 Install Docker Extension 116 -- 7.5.3 Create Docker File and Build Image 116 -- 7.6 Run Python Through Docker Container 118 -- 7.7 Exercise Problems 119 -- 8 Dataset Development 121 -- 8.1 Introduction and Requirements 121 -- 8.2 Types of Datasets 121 -- 8.3 Manual Dataset Creation 122 -- 8.3.1 Classification and Detection Dataset 122 -- 8.3.2 LabelImg Setup 123 -- 8.4 Automatic Image Collection Using Embedded Device 127 -- 8.5 Automatic Data Labeling 128 -- 8.6 Data Preprocessing and Cleaning 130 -- 8.7 Exercise Problem 131 -- 9 Training Model for Image Classification 133 -- 9.1 Problem Statement 133 -- 9.2 Default Configurations and Libraries 134 -- 9.3 Setup Data Frame Using Annotations 134 -- 9.4 Dataset Class and Methods 136 -- 9.5 Data Loader and Model Configuration 137 -- 9.6 Model Training 140 -- 9.7 Testing and Inference 141 -- 9.8 Fine-tuning 142 -- 9.8.1 Using a Different Model 144 -- 9.8.2 Using Different Optimizers and Schedulers 145 -- 9.9 Application in Embedded System 146 -- 9.10 Exercise Problems 147 -- 10 Object Detection with Classification 149 -- 10.1 Introduction 149 -- 10.2 Import Modules and Libraries 149 -- 10.3 Default Configurations and Random Seeds 151 -- 10.4 Create Data Frame and Process Labels 151 -- 10.5 Training and Validation of Transformers 152 -- 10.6 Dataset Class and Methods 153 -- 10.7 Data Loader and Classification Backbone 153 -- 10.8 Training and Validation Approach 155 -- 10.9 Run Multiple Epochs and Save the Best 156 -- 10.10 Model Inference 157 -- 10.10.1 Preprocessing Functions 158 -- 10.10.2 Inference on Test Images 160 -- 10.11 Multiple Object Detection with Classification 161 -- 10.12 Model Inference for Multiple Object Detection 163 -- 10.13 Conclusion 165 -- 10.14 Exercise Problems 167 -- 11 Deploy Deep Learning Models on Jetson Nano 169 -- 11.1 Introduction 169 -- 11.2 Pre-trained Models 169 -- 11.3 Inference on an Image File 170 -- 11.4 ONNX Model 172 -- 11.4.1 Convert PyTorch Model to ONNX 172 -- 11.5 Inference on Live Video Stream 173 -- 11.6 Conclusion 175 -- 11.7 Exercise Problem 176 -- 12 Trained PyTorch Model: From Desktop PC to Jetson Nano 177 -- 12.1 Introduction 177 -- 12.2 Model Training on a PC 177 -- 12.3 ONNX Model Inference 177 -- 12.3.1 PyTorch's.pt to ONNX Conversion 177 -- 12.3.2 Image Classification Using ONNX 179 -- 12.3.3 PyTorch's.pth to ONNX Conversion 181 -- 12.3.4 Object Detection Using ONNX 181 -- 12.4 Conclusion 186 -- 12.5 Exercise Problems 186 -- 13 Setting up Raspberry Pi 187 -- 13.1 Introduction to Raspberry Pi 187 -- 13.2 Hardware and Power Requirements 188 -- 13.2.1 Direct Setup 190 -- 13.3 Operating System Setup 190 -- 13.3.1 Flashing the OS Image 191 -- 13.3.2 Setting up OS 194 -- 13.4 Create Virtual Environment 197 -- 13.5 PyTorch and OpenCV Installation 198 -- 13.6 Other Essential Packages 201 -- 13.7 Conclusion 206 -- 13.8 Exercise Problem 207 -- 14 Deploy Deep Learning Models on Raspberry Pi 209 -- 14.1 Introduction 209 -- 14.2 Face Detection and Recognition in Video Feeds 209 -- 14.2.1 Face Detection Using HOG 209 -- 14.2.2 Face Recognition Using Transfer Learning 211 -- 14.3 Real-time Object Detection 214 -- 14.3.1 Object Detection Using YoloV3 214 -- 14.4 Real-time Classification 217 -- 14.4.1 Classification Using PyTorch's Quantized Model 217 -- 14.5 Real-time Segmentation 219 -- 14.5.1 Segmentation Using k-means Clustering 219 -- 14.6 Exercise Problems 221 -- 15 Trained PyTorch Model: From Desktop PC to Raspberry Pi 225 -- 15.1 Introduction 225 -- 15.2 Model Training on a Desktop PC 225 -- 15.3 PyTorch's.pth to ONNX 225 -- 15.3.1 ONNX Runtime 225 -- 15.3.2 Model Conversion in Raspberry Pi 5 226 -- 15.4 ONNX Model Inference on Raspberry Pi 5 228 -- 15.4.1 Import Libraries and Define Configurations 228 -- 15.4.2 Image Preprocessing 228 -- 15.4.3 Model Inference with Bounding Boxes 229 -- 15.5 Conclusion 231 -- 15.6 Exercise Problems 232 -- Index 235.
588			_aDescription based on online resource; title from digital title page (viewed on July 07, 2025).
650		0	_aDeep learning (Machine learning)
650		0	_aEmbedded computer systems _xProgramming.
650		0	_aImage processing _xDigital techniques.
650		0	_aMicrocomputers _xProgramming.
650		0	_aArtificial intelligence.
650		0	_aMechanical engineering.
650		0	_aMicroelectronics.
650		0	_aElectronic books.
650		6	_aApprentissage profond.
650		6	_aMicro-ordinateurs _xProgrammation.
650		6	_aIntelligence artificielle.
650		6	_aOrdinateurs.
650		6	_aGénie mécanique.
650		6	_aMicroélectronique.
650		6	_aLivres numériques.
650		7	_aartificial intelligence. _2aat
650		7	_acomputers. _2aat
650		7	_amechanical engineering. _2aat
650		7	_amicroelectronics. _2aat
650		7	_ae-books. _2aat
650		7	_aMicroelectronics. _2bisacsh/2024
650		7	_aComputer Vision & Pattern Recognition. _2bisacsh/2024
650		7	_aCOMPUTERS. _2bisacsh/2024
650		7	_aMechanical. _2bisacsh/2024
650		7	_aTECHNOLOGY & ENGINEERING. _2bisacsh/2024
650		7	_aElectronics. _2bisacsh/2024
650		7	_aDeep learning (Machine learning) _2fast
650		7	_aEmbedded computer systems _xProgramming _2fast
650		7	_aImage processing _xDigital techniques _2fast
650		7	_aMicrocomputers _xProgramming _2fast
655		4	_aElectronic books.
776	0	8	_iPrint version: _z9781394269266 _z1394269269 _w(DLC) 2025007090 _w(OCoLC)1453719109
776	0	8	_iPrint version: _z9781394269266
856	4	0	_uhttps://onlinelibrary.wiley.com/book/10.1002/9781394269297 _yFull text is available at Wiley Online Library. Click here to view.
901			_aYBPebook
942			_2ddc _cER