{"product_id":"138181","title":"Real-World! Deep Learning and Computer Vision: Learn Through Projects ","description":"\u003ccenter\u003e\u003cdiv style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/tmgdisk01.cafe24.com\/images\/vs\/4172\/sv\/3jXPB8rmrOW87VDrTWIfOTyDhzMeVP.png?v=1765061010\" style=\"max-width:100%;max-height:10px\"\u003e\u003c\/div\u003e\u003c\/center\u003e\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\u003ccenter\u003e\n\n\u003cdiv style=\"width:95%\"\u003e\n\n\u003cdiv style=\"text-align:center;font-size:30px;font-weight:bolder;line-height:1.6em\"\u003e Real-World! Deep Learning and Computer Vision: Learn Through Projects \u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"border-bottom:1px;border-bottom-style:dotted;border-color:;padding-bottom:20px\"\u003e\u003ccenter\u003e\u003ctable align=\"center\" width=\"100%\"\u003e\u003ctbody style=\"border:0px\"\u003e\n\n\u003ctr\u003e\u003ctd align=\"center\" style=\"line-height:1.2em;text-align:center;font-size:18px;color:black;font-weight:bold;padding-bottom:20px;\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\n\u003ctr\u003e\u003ctd style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/image.yes24.com\/goods\/150619242\/XL\" style=\"max-width:100%;height:auto\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\n\n\u003c\/tbody\u003e\u003c\/table\u003e\u003c\/center\u003e\u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"width:95%;{split_style6}padding-top:20px;padding-bottom:20px\"\u003e\n\n\u003cdiv style=\"text-align:left;font-size:16px;font-weight:bold;padding-bottom:20px\"\u003e Description \u003c\/div\u003e\n\n\u003cdiv style=\"text-align:left;word-break:break-all;font-size:14px;line-height:1.6em;\"\u003e\n\n\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eBook Introduction\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e\u003cdiv\u003e \u003cb\u003eLearn the core theories of deep learning image processing and computer vision and autonomous driving projects!\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003e\"Practical! Deep Learning Computer Vision through Projects\" is a learning resource for college students and beginner developers aspiring to become experts in deep learning computer vision and autonomous driving. It covers the core theories of deep learning image processing and provides introductory mini-projects applicable to solving related problems.\u003cbr\u003e The book is divided into three parts.\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e Part 1 covers a wide range of topics, from the basic theory of deep learning computer vision to the fundamental principles of deep neural networks and convolutional neural networks (CNNs), an introduction to the famous CNN architecture, and SSD and YOLOv4~v11 for object recognition.\u003cbr\u003e Part 2 presents a variety of projects to solve real-world problems.\u003cbr\u003e It includes classification of recyclables using a basic image classification model, CCTV object recognition using an SSD object recognition model, and crosswalk pedestrian recognition using a YOLO object recognition model. \u003cbr\u003ePart 3 shows how to do the same project in an NVIDIA Jetson Nano environment.\u003cbr\u003e This will provide readers with the knowledge necessary to implement deep learning computer vision projects in embedded environments.\u003cbr\u003e\n\n\u003c\/div\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cul\u003e\u003cli\u003e You can preview some of the book's contents.\u003cbr\u003e \u003cspan\u003ePreview\u003c\/span\u003e\n\n\u003c\/li\u003e\u003c\/ul\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eindex\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e \u003cb\u003e[PART 0] Prologue\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 0.1 What is deep learning image analysis?\u003cbr\u003e 0.2 What can you do with an AI camera?\u003cbr\u003e 0.3 Preparing for the Practice of This Book\u003cbr\u003e ___Preparing for hands-on training in Google Colab\u003cbr\u003e ___Starting the Windows Project Environment\u003cbr\u003e\u003cbr\u003e \u003cb\u003e[PART 1] Let's learn deep learning video analysis.\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e ▣ Chapter 1: Introduction to Deep Learning Image Analysis\u003cbr\u003e 1.1 Three representative deep learning image analysis techniques\u003cbr\u003e ___Image Classification\u003cbr\u003e ___Image Object Recognition\u003cbr\u003e ___image segmentation\u003cbr\u003e 1.2 Understanding Deep Learning for Image Analysis\u003cbr\u003e Deep Learning in the History of AI\u003cbr\u003e Understanding Deep Neural Networks\u003cbr\u003e 1.3 Basic Structure of a Deep Learning Model\u003cbr\u003e ___Deep Learning Training Process and Inference \u003cbr\u003e___Optimization of loss function and weights\u003cbr\u003e ___Gradient descent and backpropagation\u003cbr\u003e ___Softmax function\u003cbr\u003e 1.4 ANN MNIST PyTorch Example\u003cbr\u003e\u003cbr\u003e ▣ Chapter 2: The Beginning of Deep Learning Image Analysis, CNN\u003cbr\u003e 2.1 Why are CNNs important in deep learning image analysis?\u003cbr\u003e ___Characteristics of input data in image analysis\u003cbr\u003e ___FC layer and Conv layer\u003cbr\u003e 2.2 Understanding CNN\u003cbr\u003e ___Difference between activation maps and feature maps\u003cbr\u003e ___The role of Conv and Pooling layers\u003cbr\u003e 2.3 Preparing for the Deep Learning Training Process\u003cbr\u003e ___Prepare the dataset\u003cbr\u003e ___Activation function\u003cbr\u003e ___LeNet: CNN MNIST PyTorch Example\u003cbr\u003e\u003cbr\u003e ▣ Chapter 3: Learning Process for Deep Learning Image Analysis\u003cbr\u003e 3.1 Weight Optimization Solvers\u003cbr\u003e ___SGD + Momentum\u003cbr\u003e ___Adagrad\u003cbr\u003e ___RMSProp\u003cbr\u003e ___Adam\u003cbr\u003e 3.2 How to improve deep learning results\u003cbr\u003e ___Batch Normalization\u003cbr\u003e ___Data Augmentation and Transfer Learning\u003cbr\u003e 3.3 Popular CNN network architectures\u003cbr\u003e ___AlexNet: Winner of the First CNN-Based Image Classification Competition\u003cbr\u003e ___VGGNet: A Simple, High-Performance Network\u003cbr\u003e ___GoogLeNet: A network created by Google and used by everyone \u003cbr\u003e___ResNet: The Deepest and Highest-Performing Network\u003cbr\u003e 3.4 ResNet PyTorch Example\u003cbr\u003e ___Training a PyTorch classification model\u003cbr\u003e ___PyTorch Classification Model Inference\u003cbr\u003e\u003cbr\u003e ▣ Chapter 4: Image Segmentation and Object Recognition\u003cbr\u003e 4.1 Image Segmentation\u003cbr\u003e ___Image segmentation concept\u003cbr\u003e Evaluation criteria for the ___ model\u003cbr\u003e ___FCN image segmentation\u003cbr\u003e 4.2 Image Object Recognition\u003cbr\u003e ___Basic concepts of image object recognition\u003cbr\u003e ___Faster R-CNN\u003cbr\u003e 4.3 YOLO: The First Real-Time Object Recognition Network\u003cbr\u003e ___YOLO: You Only Look Once\u003cbr\u003e ___YOLOv2: Better, Faster, More Powerful\u003cbr\u003e ___YOLOv3: Incremental Improvement\u003cbr\u003e 4.4 SSD: Combining the strengths of Faster R-CNN and YOLO\u003cbr\u003e ___SSD: Single Shot Multi Box Detector\u003cbr\u003e 4.5 Other networks\u003cbr\u003e ___Mask R-CNN: Image Object Segmentation\u003cbr\u003e ___MobileNet v2: Small but Powerful Object Recognition\u003cbr\u003e ___YOLOv4: The new YOLO\u003cbr\u003e ___YOLOv4-tiny: Tiny version for small devices\u003cbr\u003e 4.6 YOLOv4 Practice\u003cbr\u003e ___Preparing for the practice\u003cbr\u003e ___learning\u003cbr\u003e ___Deep Neural Network Learning\u003cbr\u003e ___Testing Image Inference in Colab\u003cbr\u003e\u003cbr\u003e \u003cb\u003e[PART 2] Video Analysis Project Using Deep Learning\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003e▣ Chapter 5: Classifying Recyclables Using Image Classification\u003cbr\u003e 5.1 Overview of the Recyclable Waste Separation Project\u003cbr\u003e 5.2 Dataset Class\u003cbr\u003e ___PyTorch Custom Dataset Class\u003cbr\u003e ___Dataset class for image classification\u003cbr\u003e 5.3 Implementing a Deep Neural Network\u003cbr\u003e Import ___ module\u003cbr\u003e ___MODEL's core structure\u003cbr\u003e Implementing the __init__ method\u003cbr\u003e Implementing the ___forward method\u003cbr\u003e 5.4 Implementing a Transfer Learning Deep Neural Network\u003cbr\u003e 5.5 Deep Neural Network Learning Class\u003cbr\u003e Structure of the ___ class\u003cbr\u003e Implementing the __init__ method\u003cbr\u003e Implementing the ___prepare_network method\u003cbr\u003e Implementing the ___training_network method\u003cbr\u003e 5.6 Training with Deep Neural Networks in Colab\u003cbr\u003e ___Download Python classes and datasets\u003cbr\u003e ___learning\u003cbr\u003e Download the ___model\u003cbr\u003e 5.7 Testing Image Inference in Colab\u003cbr\u003e 5.8 Inference Practice in a Windows Environment\u003cbr\u003e ___Recyclable Classification Inference Code\u003cbr\u003e ___Running Recyclables Classification Inference\u003cbr\u003e 5.9 Summary\u003cbr\u003e\u003cbr\u003e ▣ Chapter 6: SSD Object Recognition CCTV\u003cbr\u003e 6.1 Project Goals and User Scenarios\u003cbr\u003e 6.2 Preparing for the Lab\u003cbr\u003e ___Configure Google Drive and download the source code \u003cbr\u003e___Download the Open Image Dataset\u003cbr\u003e ___Dataset class (OpenImagesDataset)\u003cbr\u003e 6.3 Network Learning\u003cbr\u003e ___SSD MobileNet v1 deep neural network\u003cbr\u003e ___SSD MobileNet v1 training class\u003cbr\u003e ___Training SSD MobileNet v1\u003cbr\u003e ___Testing Inference in Colab\u003cbr\u003e 6.4 Project Inference Practice in a Windows Environment\u003cbr\u003e ___SSD Object Recognition CCTV Inference (Video and Camera Footage Inference) Code Description\u003cbr\u003e ___SSD Object Recognition CCTV Inference Application (Object Detection Logging) Code Description\u003cbr\u003e ___SSD Object Recognition CCTV Inference Execution\u003cbr\u003e 6.5 Summary\u003cbr\u003e\u003cbr\u003e ▣ Chapter 7: Crosswalk Pedestrian Protection System Using YOLO\u003cbr\u003e 7.1 Project Goals and User Scenarios\u003cbr\u003e 7.2 Preparing for YOLOv5 Practice\u003cbr\u003e ___Prepare the dataset\u003cbr\u003e ___YOLOv5 Training\u003cbr\u003e 7.3 YOLOv5 Inference Practice on Windows\u003cbr\u003e ___Preparing YOLOv5 Inference on Windows\u003cbr\u003e ___YOLOv5 Inference Test\u003cbr\u003e 7.4 Preparing for YOLOv7 Practice\u003cbr\u003e ___YOLOv7 Description\u003cbr\u003e ___Prepare the dataset\u003cbr\u003e ___YOLOv7 Training\u003cbr\u003e YOLOv7 Inference Practice on Windows 7.5\u003cbr\u003e ___Preparing YOLOv7 inference on Windows\u003cbr\u003e ___YOLOv7 Inference Test\u003cbr\u003e 7.6 Summary\u003cbr\u003e \u003cbr\u003e\u003cb\u003e[PART 3] Using Jetson Nano and JetBot\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e ▣ Chapter 8: Jetson Nano Inference Project\u003cbr\u003e 8.1 Getting Started with Jetson Nano\u003cbr\u003e ___Jetson Nano Specifications\u003cbr\u003e ___Booting the Jetson Nano\u003cbr\u003e ___Check the basic GitHub source\u003cbr\u003e ___Jetson Nano Camera Test\u003cbr\u003e ___Check the Jetson Nano deep learning basic settings\u003cbr\u003e ___AI Acceleration Engine\u003cbr\u003e 8.2 Recyclable Classification Inference on Jetson Nano\u003cbr\u003e ___Preparing to classify recyclables\u003cbr\u003e ___Running Recyclables Classification Inference\u003cbr\u003e 8.3 SSD Object Recognition CCTV Jetson Nano Inference\u003cbr\u003e ___SSD inference test of still photos\u003cbr\u003e ___Video and Camera Image Inference Code\u003cbr\u003e ___SSD Object Recognition CCTV Inference Application (Object Detection Logging) Code Description\u003cbr\u003e ___SSD Object Recognition CCTV Inference Execution\u003cbr\u003e ___SSD Object Recognition CCTV Inference Application (Object Detection Logging) Execution\u003cbr\u003e 8.4 YOLOv4 Field Filming Black Box Jetson Nano Inference\u003cbr\u003e ___Prepare for inference\u003cbr\u003e ___Darknet Black Box Inference Code\u003cbr\u003e 8.5 YOLOv5 Crosswalk Pedestrian Protection System Jetson Nano Inference\u003cbr\u003e ___Preparing for Inference on Jetson Nano\u003cbr\u003e ___TensorRT Optimization\u003cbr\u003e Inference using ___TensorRT \u003cbr\u003e8.6 Summary\u003cbr\u003e\u003cbr\u003e ▣ Chapter 9: Three AI Mobile Robot Projects\u003cbr\u003e 9.1 Preparing for the AI ​​Mobile Robot Project\u003cbr\u003e ___AI mobile robot preparation\u003cbr\u003e ___AI Mobile Robot Basic Environment\u003cbr\u003e ___AI Mobile Robot Basic Test\u003cbr\u003e ___Power Mode Settings\u003cbr\u003e 9.2 Automatic Emergency Braking for Collision Avoidance\u003cbr\u003e ___Collecting situational data for collision avoidance\u003cbr\u003e ___Training with the AlexNet network\u003cbr\u003e ___Automatic Emergency Braking Code for Collision Avoidance\u003cbr\u003e ___AI-mobile robot automatic emergency braking test\u003cbr\u003e 9.3 Following Bot\u003cbr\u003e ___Inference Acceleration Engine Library\u003cbr\u003e ___Inference Acceleration Object Recognition Model Example Code\u003cbr\u003e ___Followingbot test code\u003cbr\u003e ___Testing a following bot with a pre-trained model\u003cbr\u003e Testing a Following Bot with an SSD model for CCTV\u003cbr\u003e 9.4 Lane-Aware Autonomous Driving\u003cbr\u003e ___Lane Data Collection (Simple Test)\u003cbr\u003e ___Training a regression model with the ResNet-18 network\u003cbr\u003e ___Lane Recognition Autonomous Driving Test (Simple Test)\u003cbr\u003e ___Lane Recognition Autonomous Driving Test Run\u003cbr\u003e ___Lane data collection and training (track testing) \u003cbr\u003e___Lane Recognition Autonomous Driving Test (Track Test)\u003cbr\u003e 9.5 Summary\u003c\/div\u003e\n\u003cdiv\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eDetailed image\u003c\/b\u003e \u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cdiv\u003e\u003cimg src=\"https:\/\/image.yes24.com\/momo\/TopCate5498\/MidCate3\/549724279.jpg\" border=\"0\" alt=\"Detailed Image 1\"\u003e\u003c\/div\u003e\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003ePublisher's Review\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e \u003cb\u003e★ What this book covers ★\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e ◎ Core Theory of Deep Learning Image Processing\u003cbr\u003e ◎ Famous CNN architecture and SSD for object recognition, YOLOv4~v11\u003cbr\u003e ◎ Image classification and object recognition practice\u003cbr\u003e ◎ Computer vision project using NVIDIA Jetson Nano\u003cbr\u003e ◎ Mobile robot (JetBot) autonomous driving project\u003cbr\u003e\u003cbr\u003e \u003cb\u003e★ YouTube channel to help you practice this book ★\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e https:\/\/www.youtube.com\/@r-ssaem \u003c\/div\u003e\n\u003cdiv\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"width:95%;padding-top:20px;padding-bottom:20px\"\u003e\n\n\u003cdiv style=\"text-align:left;font-size:16px;font-weight:bold;padding-bottom:20px\"\u003e GOODS SPECIFICS \u003c\/div\u003e\n\n\u003cdiv style=\"text-align:left;font-size:14px;line-height:1.6em;\"\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eDate of issue:\u003c\/strong\u003e August 19, 2025\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003ePage count, weight, size:\u003c\/strong\u003e 512 pages | 175*235*21mm\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eISBN13:\u003c\/strong\u003e 9791158396312 \u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\u003ccenter\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cspan\u003e\u003c\/span\u003e\n\n\u003c\/center\u003e\n\n\n\u003c\/center\u003e","brand":"LIBRAIRIE COREENNE","offers":[{"title":"Default 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