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Applied Deep Learning and Computer Vision for Self/Driving Cars
Exploreself-drivingcartechnologyusingdeeplearningandartificialintelligencetechniquesandlibrariessuchasTensorFlow,Keras,andOpenCVKeyFeatures*Buildandtrainpowerfulneuralnetworkmodelstobuildanautonomouscar*Implementcomputervision,deeplearning,andAItechniquestocreateautomotivealgorithms*OvercomethechallengesfacedwhileautomatingdifferentaspectsofdrivingusingmodernPythonlibrariesandarchitecturesBookDescriptionThankstoanumberofrecentbreakthroughs,self-drivingcartechnologyisnowanemergingsubjectinthefieldofartificialintelligenceandhasshifteddatascientists'focustobuildingautonomouscarsthatwilltransformtheautomotiveindustry.Thisbookisacomprehensiveguidetousedeeplearningandcomputervisiontechniquestodevelopautonomouscars.Startingwiththebasicsofself-drivingcars(SDCs),thisbookwilltakeyouthroughthedeepneuralnetworktechniquesrequiredtogetupandrunningwithbuildingyourautonomousvehicle.Onceyouarecomfortablewiththebasics,you'lldelveintoadvancedcomputervisiontechniquesandlearnhowtousedeeplearningmethodstoperformavarietyofcomputervisiontaskssuchasfindinglanelines,improvingimageclassification,andsoon.Youwillexplorethebasicstructureandworkingofasemanticsegmentationmodelandgettogripswithdetectingcarsusingsemanticsegmentation.Thebookalsocoversadvancedapplicationssuchasbehavior-cloningandvehicledetectionusingOpenCV,transferlearning,anddeeplearningmethodologiestotrainSDCstomimichumandriving.Bytheendofthisbook,you'llhavelearnedhowtoimplementavarietyofneuralnetworkstodevelopyourownautonomousvehicleusingmodernPythonlibraries.Whatyouwilllearn*ImplementdeepneuralnetworkfromscratchusingtheKeraslibrary*Understandtheimportanceofdeeplearninginself-drivingcars*GettogripswithfeatureextractiontechniquesinimageprocessingusingtheOpenCVlibrary*Designasoftwarepipelinethatdetectslanelinesinvideos*Implementaconvolutionalneuralnetwork(CNN)imageclassifierfortrafficsignalsigns*Trainandtestneuralnetworksforbehavioral-cloningbydrivingacarinavirtualsimulator*Discovervariousstate-of-the-artsemanticsegmentationandobjectdetectionarchitecturesWhothisbookisforIfyouareadeeplearningengineer,AIresearcher,oranyonelookingtoimplementdeeplearningandcomputervisiontechniquestobuildself-drivingblueprintsolutions,thisbookisforyou.Anyonewhowantstolearnhowvariousautomotive-relatedalgorithmsarebuilt,willalsofindthisbookuseful.Pythonprogrammingexperience,alongwithabasicunderstandingofdeeplearning,isnecessarytogetthemostofthisbook.
目錄(88章)
倒序
- coverpage
- Applied Deep Learning and Computer Vision for Self-Driving Cars
- Why subscribe?
- Contributors
- About the authors
- About the reviewers
- Packt is searching for authors like you
- Preface
- Who this book is for
- What this book covers
- To get the most out of this book
- Get in touch
- Section 1: Deep Learning Foundation and SDC Basics
- The Foundation of Self-Driving Cars
- Introduction to SDCs
- Challenges in current deployments
- Levels of autonomy
- Deep learning and computer vision approaches for SDCs
- Summary
- Dive Deep into Deep Neural Networks
- Diving deep into neural networks
- Understanding neurons and perceptrons
- The workings of ANNs
- Understanding activation functions
- The cost function of neural networks
- Optimizers
- Understanding hyperparameters
- TensorFlow versus Keras
- Summary
- Implementing a Deep Learning Model Using Keras
- Starting work with Keras
- Keras for deep learning
- Building your first deep learning model
- Summary
- Section 2: Deep Learning and Computer Vision Techniques for SDC
- Computer Vision for Self-Driving Cars
- Introduction to computer vision
- Building blocks of an image
- Color space techniques
- Introduction to convolution
- Edge detection and gradient calculation
- Image transformation
- Summary
- Finding Road Markings Using OpenCV
- Finding road markings in an image
- Detecting road markings in a video
- Summary
- Improving the Image Classifier with CNN
- Images in computer format
- Introducing CNNs
- Introduction to handwritten digit recognition
- Summary
- Road Sign Detection Using Deep Learning
- Dataset overview
- Loading the data
- Image exploration
- Data preparation
- Model training
- Model accuracy
- Summary
- Section 3: Semantic Segmentation for Self-Driving Cars
- The Principles and Foundations of Semantic Segmentation
- Introduction to semantic segmentation
- Understanding the semantic segmentation architecture
- Overview of different semantic segmentation architectures
- Summary
- Implementing Semantic Segmentation
- Semantic segmentation in images
- Semantic segmentation in videos
- Summary
- Section 4: Advanced Implementations
- Behavioral Cloning Using Deep Learning
- Neural network for regression
- Behavior cloning using deep learning
- Summary
- Vehicle Detection Using OpenCV and Deep Learning
- What makes YOLO different?
- The YOLO loss function
- The YOLO architecture
- Implementation of YOLO object detection
- Summary
- Next Steps
- SDC sensors
- Introduction to sensor fusion
- Kalman filter
- Summary
- Other Books You May Enjoy
- Leave a review - let other readers know what you think 更新時間：2021-04-09 23:13:26
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