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Applied deep learning and computer v...

Ranjan, Sumit,

Applied deep learning and computer vision for self-driving cars :build autonomous vehicles using deep neural networks and behavior-cloning techniques /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Applied deep learning and computer vision for self-driving cars :Sumit Ranjan, S. Senthamilarasu.
Reminder of title:	build autonomous vehicles using deep neural networks and behavior-cloning techniques /
Author:	Ranjan, Sumit,
other author:	Senthamilarasu, S.,
Published:	Birmingham :Packt Publishing,2020.
Description:	vi, [307] p. :ill. ;24 cm.
Notes:	Includes index.
Notes:	Table of ContentsThe Foundation of Self-Driving CarsDive Deep into Deep Neural Networks Implementing a Deep Learning Model using KerasComputer Vision for Self-Driving CarsFinding Road Markings using OpenCVImproving the Image Classifier with CNNRoad Sign Detection using Deep LearningThe Principles and Foundations of Semantic SegmentationImplementation of Semantic Segmentation Behavior Cloning using Deep Learning Vehicle Detection using OpenCV and Deep Learning Next Steps.
Subject:	Automated vehiclesComputer simulation.
ISBN:	9781838646301 (pbk.) :

Applied deep learning and computer vision for self-driving cars :build autonomous vehicles using deep neural networks and behavior-cloning techniques /
Ranjan, Sumit,

Applied deep learning and computer vision for self-driving cars :build autonomous vehicles using deep neural networks and behavior-cloning techniques /Sumit Ranjan, S. Senthamilarasu. - Birmingham :Packt Publishing,2020. - vi, [307] p. :ill. ;24 cm.

Includes index.

This book teaches you the different techniques and methodologies associated while implementing deep learning solutions in self-driving cars. You will use real-world examples to implement various neural network architectures to develop your own autonomous and automated vehicle using the Python environment.

ISBN: 9781838646301 (pbk.) :NT$1174

Nat. Bib. No.: GBC0C5092bnb

Nat. Bib. Agency Control No.: 019898250UkSubjects--Topical Terms:

913743
Automated vehicles
--Computer simulation.

LC Class. No.: TL152.8

Dewey Class. No.: 629.0460285631

Applied deep learning and computer vision for self-driving cars :build autonomous vehicles using deep neural networks and behavior-cloning techniques /
LDR:01650cam a2200253 a 450 001 615057
003 OCoLC
005 20220720053433.0
008 220808s2020 enka 001 0 eng d
015 $a GBC0C5092 $2 bnb
016 7 $a 019898250 $2 Uk
020 $a 9781838646301 (pbk.) : $c NT$1174
020 $z 9781838647025 (PDF ebook)
035 $a (OCoLC)1191705756
035 $a on1191705756
040 $a YDX $b eng $c YDX $d UKMGB $d OCLCO $d OCLCF $d TSU $d OCLCQ $d OCLCO
050 4 $a TL152.8
082 0 4 $a 629.0460285631 $2 23
100 1 $a Ranjan, Sumit, $e author. $3 913741
245 1 0 $a Applied deep learning and computer vision for self-driving cars : $b build autonomous vehicles using deep neural networks and behavior-cloning techniques / $c Sumit Ranjan, S. Senthamilarasu.
260 $a Birmingham : $b Packt Publishing, $c 2020.
300 $a vi, [307] p. : $b ill. ; $c 24 cm.
500 $a Includes index.
500 $a Table of ContentsThe Foundation of Self-Driving CarsDive Deep into Deep Neural Networks Implementing a Deep Learning Model using KerasComputer Vision for Self-Driving CarsFinding Road Markings using OpenCVImproving the Image Classifier with CNNRoad Sign Detection using Deep LearningThe Principles and Foundations of Semantic SegmentationImplementation of Semantic Segmentation Behavior Cloning using Deep Learning Vehicle Detection using OpenCV and Deep Learning Next Steps.
520 $a This book teaches you the different techniques and methodologies associated while implementing deep learning solutions in self-driving cars. You will use real-world examples to implement various neural network architectures to develop your own autonomous and automated vehicle using the Python environment.
650 0 $a Automated vehicles $x Computer simulation. $3 913743
650 0 $a Machine learning. $3 188639
650 0 $a Neural networks (Computer science) $3 181982
700 1 $a Senthamilarasu, S., $e author. $3 913742
776 0 8 $i Ebook version : $z 9781838647025