English | 2020 | ISBN-13: 978-1838646301 | 332 Pages | True (EPUB, MOBI) + Code | 137 MB

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.
Table of Contents

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