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  • Book Overview & Buying Applied Deep Learning and Computer Vision for Self-Driving Cars
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Applied Deep Learning and Computer Vision for Self-Driving Cars

Applied Deep Learning and Computer Vision for Self-Driving Cars

By : Sumit Ranjan, Dr. S. Senthamilarasu
4.3 (9)
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Applied Deep Learning and Computer Vision for Self-Driving Cars

Applied Deep Learning and Computer Vision for Self-Driving Cars

4.3 (9)
By: Sumit Ranjan, Dr. S. Senthamilarasu

Overview of this book

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.
Table of Contents (18 chapters)
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1
Section 1: Deep Learning Foundation and SDC Basics
5
Section 2: Deep Learning and Computer Vision Techniques for SDC
10
Section 3: Semantic Segmentation for Self-Driving Cars
13
Section 4: Advanced Implementations

Software programming

Another challenge is programming for safety and practicality, which are often at odds with each other. If we program a vehicle purely for safety, its safest option is not to drive. Driving is an inherently dangerous operation, and programming for the multiple scenarios that arise while driving is an insanely difficult task. It is easy to say follow the rules of the road, but the problem is, humans don't follow the rules of the road perfectly. Therefore, programmers need to enable SDCs to react to this. Sometimes, the computer will need to make difficult decisions and may need to make a decision that involves endangering the life of its occupants or people outside the vehicle. This is a dangerous task, but if we continue improving on the technology, we could start to see reduced road deaths, all while making taxi services drastically cheaper and freeing many people from the financial burden of purchasing a vehicle.

Tesla is in a fantastic position to gradually update their software as they master each scenario. They don't need to create the perfect SDC out of the gate, and with this latest computer, they are going to be able to continue their technological growth.

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Applied Deep Learning and Computer Vision for Self-Driving Cars
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