With self-driving cars (SDCs) being an emerging subject in the field of artificial intelligence, data scientists have now focused their interest on building autonomous cars. This book is a comprehensive guide to using deep learning and computer vision techniques to develop SDCs.

The book starts by covering the basics of SDCs and deep neural network techniques that are required to get up and running with building your autonomous car. Once you are comfortable with the basics, you'll learn how to implement the convolution neural network. As you advance, you'll use deep learning methods to perform a variety of tasks such as finding lane lines, improving the image classifier, and road sign detection. Furthermore, you'll delve into the basic structure and workings of a semantic segmentation model, and even get to grips with detecting cars using semantic segmentation. The book also covers advanced applications such as behavior cloning and vehicle detection using OpenCV and advance deep learning methodologies.

By the end of this book, you'll have learned how to implement various neural networks to develop autonomous vehicle solutions using modern libraries from the Python environment.