nach der Bestellung gedruckt Neuware - Printed after ordering - Explore self-driving car technology using deep learning and artificial intelligence techniques and libraries such as TensorFlow, Keras, and OpenCVKey Features Build and train powerful neural network models to build an autonomous car Implement computer vision, deep learning, and AI techniques to create automotive algorithms Overcome the challenges faced while automating different aspects of driving using modern Python libraries and architecturesBook DescriptionThanks to a number of recent breakthroughs, self-driving car technology is now an emerging subject in the field of artificial intelligence and has shifted data scientists' focus to building autonomous cars that will transform the automotive industry. This book is a comprehensive guide to use deep learning and computer vision techniques to develop autonomous cars.Starting with the basics of self-driving cars (SDCs), this book will take you through the deep neural network techniques required to get up and running with building your autonomous vehicle. Once you are comfortable with the basics, you'll delve into advanced computer vision techniques and learn how to use deep learning methods to perform a variety of computer vision tasks such as finding lane lines, improving image classification, and so on. You will explore the basic structure and working of a semantic segmentation model and get to grips with detecting cars using semantic segmentation. The book also covers advanced applications such as behavior cloning and vehicle detection using OpenCV, transfer learning, and deep learning methodologies to train SDCs to mimic human driving.By the end of this book, you'll have learned how to implement a variety of neural networks to develop your own autonomous vehicle using modern Python libraries.What you will learn Implement deep neural network from scratch using the Keras library Understand the importance of deep learning in self-driving cars Get to grips with feature extraction techniques in image processing using the OpenCV library Design a software pipeline that detects lane lines in videos Implement a convolutional neural network (CNN) image classifier for traffic signal signs Train and test neural networks for behavioral cloning by driving a car in a virtual simulator Discover various state-of-the-art semantic segmentation and object detection architecturesWho this book is forIf you are a deep learning engineer, AI researcher, or anyone looking to implement deep learning and computer vision techniques to build self-driving blueprint solutions, this book is for you. Anyone who wants to learn how various automotive-related algorithms are built, will also find this book useful. Python programming experience, along with a basic understanding of deep learning, is necessary to get the most of this book.

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Detalles bibliogr�ficos

T�tulo: Applied Deep Learning and Computer Vision for Self-Driving Cars : Build autonomous vehicles using deep neural networks and behavior-cloning techniques
Editor: Packt Publishing
Fecha de publicaci�n: 2020
Idioma: Ingl�s
ISBN 10: 1838646302
ISBN 13: 9781838646301
Encuadernaci�n: Taschenbuch
Estado: Neu
Dimensiones: 235�mil�metros de ancho por 191 de alto por 19 de largo
Peso: 622 gramos

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Sinopsis

Explore self-driving car technology using deep learning and artificial intelligence techniques and libraries such as TensorFlow, Keras, and OpenCV

Key Features

Build and train powerful neural network models to build an autonomous car
Implement computer vision, deep learning, and AI techniques to create automotive algorithms
Overcome the challenges faced while automating different aspects of driving using modern Python libraries and architectures

Book Description

Thanks to a number of recent breakthroughs, self-driving car technology is now an emerging subject in the field of artificial intelligence and has shifted data scientists' focus to building autonomous cars that will transform the automotive industry. This book is a comprehensive guide to use deep learning and computer vision techniques to develop autonomous cars.

Starting with the basics of self-driving cars (SDCs), this book will take you through the deep neural network techniques required to get up and running with building your autonomous vehicle. Once you are comfortable with the basics, you'll delve into advanced computer vision techniques and learn how to use deep learning methods to perform a variety of computer vision tasks such as finding lane lines, improving image classification, and so on. You will explore the basic structure and working of a semantic segmentation model and get to grips with detecting cars using semantic segmentation. The book also covers advanced applications such as behavior-cloning and vehicle detection using OpenCV, transfer learning, and deep learning methodologies to train SDCs to mimic human driving.

By the end of this book, you'll have learned how to implement a variety of neural networks to develop your own autonomous vehicle using modern Python libraries.

What you will learn

Implement deep neural network from scratch using the Keras library
Understand the importance of deep learning in self-driving cars
Get to grips with feature extraction techniques in image processing using the OpenCV library
Design a software pipeline that detects lane lines in videos
Implement a convolutional neural network (CNN) image classifier for traffic signal signs
Train and test neural networks for behavioral-cloning by driving a car in a virtual simulator
Discover various state-of-the-art semantic segmentation and object detection architectures

Who this book is for

If you are a deep learning engineer, AI researcher, or anyone looking to implement deep learning and computer vision techniques to build self-driving blueprint solutions, this book is for you. Anyone who wants to learn how various automotive-related algorithms are built, will also find this book useful. Python programming experience, along with a basic understanding of deep learning, is necessary to get the most of this book.

The Foundation of Self-Driving Cars
Dive Deep into Deep Neural Networks
Implementing a Deep Learning Model using Keras
Computer Vision for Self-Driving Cars
Finding Road Markings using OpenCV
Improving the Image Classifier with CNN
Road Sign Detection using Deep Learning
The Principles and Foundations of Semantic Segmentation
Implementation of Semantic Segmentation
Behavior Cloning using Deep Learning
Vehicle Detection using OpenCV and Deep Learning
Next Steps

Acerca de los autores

Sumit Ranjan is a silver medalist in his Bachelor of Technology (Electronics and Telecommunication) degree. He is a passionate data scientist who has worked on solving business problems to build an unparalleled customer experience across domains such as, automobile, healthcare, semi-conductor, cloud-virtualization, and insurance. He is experienced in building applied machine learning, computer vision, and deep learning solutions, to meet real-world needs. He was awarded Autonomous Self-Driving Car Scholar by KPIT Technologies. He has also worked on multiple research projects at Mercedes Benz Research and Development. Apart from work, his hobbies are traveling and exploring new places, wildlife photography, and blogging.

Dr. S. Senthamilarasu was born and raised in the Coimbatore, Tamil Nadu. He is a technologist, designer, speaker, storyteller, journal reviewer educator, and researcher. He loves to learn new technologies and solves real world problems in the IT industry. He has published various journals and research papers and has presented at various international conferences. His research areas include data mining, image processing, and neural network. He loves reading Tamil novels and involves himself in social activities. He has also received silver medals in international exhibitions for his research products for children with an autism disorder. He currently lives in Bangalore and is working closely with lead clients.

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