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Applied Deep Learning and Computer Vision for Self-Driving Cars

You're reading from  Applied Deep Learning and Computer Vision for Self-Driving Cars

Product type Book
Published in Aug 2020
Publisher Packt
ISBN-13 9781838646301
Pages 332 pages
Edition 1st Edition
Languages
Authors (2):
Sumit Ranjan Sumit Ranjan
Profile icon Sumit Ranjan
Dr. S. Senthamilarasu Dr. S. Senthamilarasu
Profile icon Dr. S. Senthamilarasu
View More author details

Table of Contents (18) Chapters

Preface 1. Section 1: Deep Learning Foundation and SDC Basics
2. The Foundation of Self-Driving Cars 3. Dive Deep into Deep Neural Networks 4. Implementing a Deep Learning Model Using Keras 5. Section 2: Deep Learning and Computer Vision Techniques for SDC
6. Computer Vision for Self-Driving Cars 7. Finding Road Markings Using OpenCV 8. Improving the Image Classifier with CNN 9. Road Sign Detection Using Deep Learning 10. Section 3: Semantic Segmentation for Self-Driving Cars
11. The Principles and Foundations of Semantic Segmentation 12. Implementing Semantic Segmentation 13. Section 4: Advanced Implementations
14. Behavioral Cloning Using Deep Learning 15. Vehicle Detection Using OpenCV and Deep Learning 16. Next Steps 17. Other Books You May Enjoy

Diving deep into neural networks

Deep learning is a sub-field of ML that is based on ANNs (see Fig 2.1). Deep learning mimics the human brain and is inspired by the structure and function of the brain. The concept of deep learning is not new and has existed for a number of years. The reason for the popularity and success of deep learning in recent years is due to high powered processing units, such as GPUs, and the presence of enormous amounts of data. One of the reasons for deep neural networks (DNNs) performing better is the complex relationships among features and high-dimensional data:

Fig 2.1: Deep learning is a sub-field of ML

One of the great things about deep learning is that it eliminates human input. It replaces the costly and inefficient effort of human beings and automates most of the extraction process from features and raw data so that it doesn't require human involvement. Before, we used to extract features ourselves to make ML algorithms...

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