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Key benefits

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

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.

Who is this book 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.

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

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

Frequently bought together

₱3367.99

Hands-On Vision and Behavior for Self-Driving Cars

₱2806.99

Applied Deep Learning and Computer Vision for Self-Driving Cars

₱2500.99

Total ₱ 8,675.97

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Amazon verified reviews

A T M Azharul Islam Jul 17, 2021

I am happy with that

Amazon Verified review

Imran Sarker Oct 25, 2020

Being an avid learner of self-driving cars, I read many research papers to learn the basic concept of Autonomous cars. Those papers are a mostly advanced concept that made me realize to look into a book that can break down the advanced concept. Finally, I found this book. The authors explained every section very well. The good thing about the book is it has practical implementation with many self-driving car projects. Besides, Learners can grab the idea of different neural networks.Summary: I highly recommend this book for the readers who wants to learn Self Driving Cars from basic to advanced level.

Sudheer Jan 29, 2021

It is a very well written book that introduces beginners like myself to the various technologies used in autonomous vehicles. I got an understanding of the make up of what goes into designing a self driving car & how standards are still evolving due to complexities, differences in hardware, and computational aspects. I particularly enjoyed reading Section 2 about "Computer Vision Techniques" and Section 3 about "Behavioral Cloning".#deeplearning #ai #machinelearning #artificialintelligence #computervision #selfdrivingcars #autonomousvehicles #autonomous

Jérémie Ohana Nov 12, 2021

Très intéressant

Sam Oct 29, 2020

This book keeps you engrossed with charts, graphs and pictures, not to mention, plenty of code to get stated with. Without delving too much into the mathematical details, this book manages to give you the fundamental principal and the breath of technical know-how required to embark into the field of self-driving-car.Pros: 1. Very well structured. 2. Plenty of code, charts, graphs to keep the reader interested. 3. General introduction without getting into the nitty gritty helps the reader to get the overall picture without being lost into the specifics.Cons: 1. I would agree with other reviewers about their critic of the book not having a end-to-end project connecting the sections.While this is an introductory book, a section dedicated to discussing some of the latest research with deep learning in self-driving-cars could have ushered a more intrigued reader. Kudos to the authors for taking the time to write the book.

Applied Deep Learning and Computer Vision for Self-Driving Cars: Build autonomous vehicles using deep neural networks and behavior-cloning techniques

What do you get with eBook?